Cloud-based fleet and asset management for edge computing of machine learning and artificial intelligence workloads

ABSTRACT

A process can include receiving monitoring information associated with a machine learning (ML) or artificial intelligence (AI) workload implemented by an edge compute unit of a plurality of edge compute units. Status information corresponding to a plurality of connected edge assets can be received, the plurality of edge compute units and connected edge assets included in a fleet of edge devices. A remote fleet management graphical user interface (GUI) can display a portion of the monitoring or status information for a subset of the fleet of edge devices, based on a user selection input, and can receive a user configuration input indicative of an updated configuration for at least one workload corresponding to a pre-trained ML or AI model deployed on the at least one edge compute unit. A cloud computing environment can transmit control information corresponding to the updated configuration to the at least one edge compute unit.

TECHNICAL FIELD

The present disclosure pertains to edge computing, and more specificallypertains to systems and techniques for high-performance edge computingand management thereof.

BACKGROUND

Edge computing is a distributed computing paradigm that can be used todecentralize data processing and other computational operations bybringing compute capability and data storage closer to the edge (e.g.,the location where the compute and/or data storage is needed, often atthe “edge” of a network such as the internet). Edge computing systemsare often provided in the same location where input data is generatedand/or in the same location where an output result of the computationaloperations is needed. The use of edge computing systems can reducelatency and bandwidth usage, as data is ingested and processed locallyat the edge and rather than being transmitted to a more centralizedlocation for processing.

In many existing cloud computing architectures, data generated atendpoints (e.g., mobile devices, Internet of Things (IoT) sensors,robots, industrial automation systems, security cameras, etc., amongvarious other edge devices and sensors) is transmitted to centralizeddata centers for processing. The processed results are then transmittedfrom the centralized data centers to the endpoints requesting theprocessed results. The centralized processing approach may presentchallenges for growing use cases, such as for real-time applicationsand/or artificial intelligence (AI) and machine learning (ML) workloads.For instance, centralized processing models and conventional cloudcomputing architectures can face constraints in the areas of latency,availability, bandwidth usage, data privacy, network security, and thecapacity to process large volumes of data in a timely manner.

In the context of edge computing, the “edge” refers to the edge of thenetwork, close to the endpoint devices and the sources of data. In anedge computing architecture, computation and data storage aredistributed across a network of edge nodes that are near the endpointdevices and sources of data. The edge nodes can be configured to performvarious tasks relating to data processing, storage, analysis, etc. Basedon using the edge nodes to process data locally, the amount of data thatis transferred from the edge to the cloud (or other centralized datacenter) can be significantly reduced. Accordingly, the use of edgecomputing has become increasingly popular for implementing a diverserange of AI and ML applications, as well as for serving other use casesthat demand real-time processing, minimal latency, high availability,and high reliability. In general, such applications and use cases mayrely on high-bandwidth sensors that have the ability to generate data atmassive rates (e.g., on the order of 50 Gbit/sec or 22 TB/hr).

BRIEF SUMMARY

In some examples, systems and techniques are described for implementingfleet management (e.g., a fleet of edge compute units) and/or assetmanagement (e.g., connected sensors and other assets at the edge) forhigh-performance edge computing, including edge computing for machinelearning (ML) and/or artificial intelligence (AI) deployments and/orworkloads.

According to at least one illustrative example, a method is provided,the method comprising: receiving monitoring information from eachrespective edge compute unit of a plurality of edge compute units,wherein the monitoring information includes information associated withone or more machine learning (ML) or artificial intelligence (AI)workloads implemented by the respective edge compute unit; receivingrespective status information corresponding to a plurality of connectededge assets, wherein each connected edge asset is associated with one ormore edge compute units of the plurality of edge compute units, andwherein the plurality of edge compute units and the plurality ofconnected edge assets are included in a fleet of edge devices;displaying, using a remote fleet management graphical user interface(GUI), at least a portion of the monitoring information or the statusinformation corresponding to a selected subset of the fleet of edgedevices, wherein the selected subset is determined based on one or moreuser selection inputs to the remote fleet management GUI; receiving,using the remote fleet management GUI, one or more user configurationinputs indicative of an updated configuration for at least one workloadof at least one edge compute unit of the selected subset of the fleet ofedge devices, the at least one workload corresponding to a pre-trainedML or AI model deployed on the at least one edge compute unit; andtransmitting, from a cloud computing environment associated with theremote fleet management GUI, control information corresponding to theupdated configuration, wherein the control information is transmitted tothe at least one edge compute unit of the selected subset.

As used herein, the terms “user equipment” (UE) and “network entity” arenot intended to be specific or otherwise limited to any particular radioaccess technology (RAT), unless otherwise noted. In general, a UE may beany wireless communication device (e.g., a mobile phone, router, tabletcomputer, laptop computer, and/or tracking device, etc.), wearable(e.g., smartwatch, smart-glasses, wearable ring, and/or an extendedreality (XR) device such as a virtual reality (VR) headset, an augmentedreality (AR) headset or glasses, or a mixed reality (MR) headset),vehicle (e.g., automobile, motorcycle, bicycle, etc.), robotic system(e.g., autonomous passenger vehicle, unmanned aircraft system (UAS),uncrewed ground vehicle (UGV), mobile robotic platform, uncrewedsubmersible, biped or multi-legged robot, cobot, industrial automation,articulated arm, etc.), and/or Internet of Things (IoT) device, etc.,used by a user to communicate over a wireless communications network. AUE may be mobile or may (e.g., at certain times) be stationary, and maycommunicate with a radio access network (RAN). As used herein, the term“UE” may be referred to interchangeably as an “access terminal” or “AT,”a “client device,” a “wireless device,” a “subscriber device,” a“connected device,” a “subscriber terminal,” a “subscriber station,” a“user terminal” or “UT,” a “mobile device,” a “mobile terminal,” a“mobile station,” or variations thereof. Generally, UEs can communicatewith a core network via a RAN, and through the core network the UEs canbe connected with external networks such as the Internet and with otherUEs. Of course, other mechanisms of connecting to the core networkand/or the Internet are also possible for the UEs, such as over wiredaccess networks, wireless local area network (WLAN) networks (e.g.,based on IEEE 802.11 communication standards, etc.) and so on.

The term “network entity” or “base station” may refer to a singlephysical Transmission-Reception Point (TRP) or to multiple physicalTransmission-Reception Points (TRPs) that may or may not be co-located.For example, where the term “network entity” or “base station” refers toa single physical TRP, the physical TRP may be an antenna of a basestation (e.g., satellite constellation ground station/internet gateway)corresponding to a cell (or several cell sectors) of the base station.Where the term “network entity” or “base station” refers to multipleco-located physical TRPs, the physical TRPs may be an array of antennas(e.g., as in a multiple-input multiple-output (MIMO) system or where thebase station employs beamforming) of the base station. Where the term“base station” refers to multiple non-co-located physical TRPs, thephysical TRPs may be a distributed antenna system (DAS) (a network ofspatially separated antennas connected to a common source via atransport medium) or a remote radio head (RRH) (a remote base stationconnected to a serving base station). Because a TRP is the point fromwhich a base station transmits and receives wireless signals, as usedherein, references to transmission from or reception at a base stationare to be understood as referring to a particular TRP of the basestation.

An RF signal comprises an electromagnetic wave of a given frequency thattransports information through the space between a transmitter and areceiver. As used herein, a transmitter may transmit a single “RFsignal” or multiple “RF signals” to a receiver. However, the receivermay receive multiple “RF signals” corresponding to each transmitted RFsignal due to the propagation characteristics of RF signals throughmultipath channels. The same transmitted RF signal on different pathsbetween the transmitter and receiver may be referred to as a “multipath”RF signal. As used herein, an RF signal may also be referred to as a“wireless signal” or simply a “signal” where it is clear from thecontext that the term “signal” refers to a wireless signal or an RFsignal.

This summary is not intended to identify key or essential features ofthe claimed subject matter, nor is it intended to be used in isolationto determine the scope of the claimed subject matter. The subject mattershould be understood by reference to appropriate portions of the entirespecification of this patent, any or all drawings, and each claim.

The foregoing, together with other features and embodiments, will becomemore apparent upon referring to the following specification, claims, andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the disclosure can be obtained, a moreparticular description of the principles briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. The use of a same referencenumbers in different drawings indicates similar or identical items orfeatures. Understanding that these drawings depict only exemplaryembodiments of the disclosure and are not therefore to be considered tobe limiting of its scope, the principles herein are described andexplained with additional specificity and detail through the use of theaccompanying drawings in which:

FIG. 1 depicts an example design of a base station and a user equipment(UE) for transmission and processing of signals exchanged between the UEand the base station, in accordance with some examples;

FIG. 2 is a diagram illustrating an example configuration of aNon-Terrestrial Network (NTN) for providing data network connectivity toterrestrial (ground-based) devices, in accordance with some examples;

FIG. 3 is a diagram illustrating an example of a satellite internetconstellation network that can be used to provide low latency satelliteinternet connectivity, in accordance with some examples;

FIG. 4 is a diagram illustrating an example of an edge computing systemfor machine learning (ML) and/or artificial intelligence (AI) workloads,where the edge computing system includes one or more local sites eachhaving one or more edge compute units, in accordance with some examples;

FIG. 5 is a diagram illustrating an example software stack associatedwith implementing an edge computing system for ML and/or AI workloads,in accordance with some examples;

FIG. 6 is a diagram illustrating an example architecture forimplementing global services and edge compute services of an edgecomputing system for ML and/or AI workloads, in accordance with someexamples;

FIG. 7 is a diagram illustrating an example infrastructure andarchitecture for implementing an edge compute unit of an edge computingsystem for ML and/or AI workloads, in accordance with some examples;

FIG. 8 is a diagram illustrating an example graphical user interface(GUI) of a global management console associated with asset managementand telemetry observation for a fleet of edge compute units of an edgecomputing system for ML and/or AI workloads, in accordance with someexamples;

FIG. 9 is a diagram illustrating another example GUI of a globalmanagement console associated with asset management and telemetryobservation for a fleet of edge compute units of an edge computingsystem for ML and/or AI workloads, in accordance with some examples; and

FIG. 10 is a block diagram illustrating an example of a computing systemarchitecture that can be used to implement one or more aspects describedherein, in accordance with some examples.

DETAILED DESCRIPTION

Certain aspects of this disclosure are provided below for illustrationpurposes. Alternate aspects may be devised without departing from thescope of the disclosure. Additionally, well-known elements of thedisclosure will not be described in detail or will be omitted so as notto obscure the relevant details of the disclosure. Some of the aspectsdescribed herein may be applied independently and some of them may beapplied in combination as would be apparent to those of skill in theart. In the following description, for the purposes of explanation,specific details are set forth in order to provide a thoroughunderstanding of aspects of the application. However, it will beapparent that various aspects may be practiced without these specificdetails. The figures and description are not intended to be restrictive.

The ensuing description provides example aspects only, and is notintended to limit the scope, applicability, or configuration of thedisclosure. Rather, the ensuing description of the example aspects willprovide those skilled in the art with an enabling description forimplementing an example aspect. It should be understood that variouschanges may be made in the function and arrangement of elements withoutdeparting from the scope of the application as set forth in the appendedclaims.

Overview

Systems, apparatuses, methods (also referred to as processes), andcomputer-readable media (collectively referred to herein as “systems andtechniques”) are described herein for high-performance edge computingfor machine learning (ML) and artificial intelligence (AI) workloads. Asused herein, reference to an “ML workload” includes both workloadsimplemented using a trained machine learning model and workloadsimplemented using a trained artificial intelligence model. Similarly,reference to an “AI workload” includes both workloads implemented usinga trained artificial intelligence model and workloads implemented usinga trained machine learning model.

In some aspects, one or more edge compute units (also referred to as a“fleet” of edge compute units) can be used to implement high-performanceedge computing for ML and AI workloads. The edge compute unit caninclude modular and/or configurable compute hardware units (e.g., CPUs,GPUs, TPUs, NPUs, accelerators, memory, storage, etc.) for running thetrained ML or AI models. In some cases, the edge compute unit can be adata center unit that is deployable to the edge. An edge compute unit(e.g., containerized data center or containerized compute unit) can beconfigured according to an intended use case and/or according to one ormore deployment site location characteristics. For example, the edgecompute unit can be a containerized data center having various hardwareconfigurations (e.g., CPU-centric or GPU-centric compute hardwareconfiguration; urban location or remote location configuration; privateelectrical/data network or utility electrical/data network connectivityconfiguration; etc.).

The containerized edge compute unit can be deployed to a user-determined(e.g., enterprise-determined) site location. The enterprise sitelocation may have varying levels of existing infrastructureavailability, based upon which the containerized edge compute unit canbe correspondingly configured. For example, the containerized edgecompute unit can be configured based at least in part on the electricalinfrastructure and data connectivity infrastructure availability at theenterprise site location. The containerized edge compute unit can bepre-configured (at the time of deployment to the enterprise sitelocation) with hardware infrastructure, data connectivity, and criticalenvironmental systems fully or partially integrated.

In one illustrative example, the containerized edge compute unit can bepre-configured (e.g., pre-loaded) with a fleet management, knowledgebases, predetermined datasets and trained ML/AI models and applicationsoftware stack for edge computing AI and ML workloads, such as the fleetmanagement and application software stack that is the subject of thisdisclosure and will be described in greater depth below.

The presently disclosed fleet management and application software stackis also referred to herein as an “edge ML/AI platform” or an “edge ML/AImanagement system.” In some embodiments, the edge ML/AI platform caninclude at least a remote fleet management engine, a telemetry andmonitoring observation engine, a platform application orchestrationengine, and a deployable application repository, each of which aredescribed in greater detail with respect to the figures, and withparticular reference to the examples of FIGS. 5-7 .

In one illustrative example, the remote fleet management (e.g., command)engine, telemetry and monitoring (e.g., observation) engine, platformapplication orchestration engine, and deployable application repositorycan be implemented using a single, global management console of thepresently disclosed edge ML/AI platform. For instance, the globalmanagement console can be configured to provide a single pane of glassinterface that provides a unified data presentation view andbidirectional interaction across the various sources and constituentengines included in or otherwise associated with the presently disclosededge ML/AI platform. As used herein, the global management “console” canalso be referred to as a global management “portal.” Further details ofthe global management console are described below with respect to thefigures, and with particular reference to the examples of FIGS. 8 and 9.

In some embodiments, the presently disclosed edge ML/AI platform can beused to implement a connected edge and cloud for ML and AI workloads.For instance, many ML and AI workloads, applications, and/or models aredata-intensive and benefit from continual (or relatively frequent)retraining to account for data drift and model degradation. In somecases, the ML and AI workloads may require monitoring of modeldegradation in conjunction with regular training (e.g., retraining, finetuning, instruction tuning, continual learning, etc.) with new data,model parameters, model hyperparameters, etc., as appropriate. In oneillustrative example, the systems and techniques described herein can beused to provide an edge ML/AI monitoring and management platformconfigured to provide streamlined and efficient operations fordeploying, maintaining, and updating ML and AI workloads or models tothe edge.

As described in greater detail herein, the edge ML/AI monitoring andmanagement platform can be implemented for a fleet of edge compute units(e.g., the containerized edge compute units described above) and aplurality of connected sensors and/or edge assets associated with atleast one edge compute unit of the fleet. The containerized edge computeunits of the fleet can be used to provide local (e.g., edge) storage,inference, prediction, response, etc. using trained ML and/or AI modelsfrom a centralized or cloud distribution source, where the trainedmodels are trained or fine-tuned remotely from the edge compute unit.For instance, the edge ML/AI monitoring and management platform can beused to implement or otherwise interface to ML/AI training clustersrunning in the cloud. The edge compute units can subsequently run orimplement their respective trained models locally (e.g., onboard theedge compute unit), using as input the data obtained from local sensorsor other endpoint assets associated with the edge compute unit.

In some embodiments, the edge ML/AI monitoring and management platformcan be used to implement and/or orchestrate a hub-and-spoke architecturefor efficient ML/AI inferencing at the edge. For instance, ML and AImodel training often necessitates massive amounts of data and processingpower, with the resultant trained model quality being highly correlatedwith the size and diversity of the training and test data. Traininglarge models can require running thousands of GPUs and ingestinghundreds of terabytes of data, over the course of several weeks.Accordingly, large-scale ML and AI model training may be better suitedfor deployment on cloud and/or on-premises infrastructure (e.g.,centralized large-scale compute infrastructure). By comparison, ML andAI inferencing (e.g., performing inference using a trained ML or AImodel) utilizes a relatively smaller amount of compute resources (e.g.,CPU, GPU, memory, etc.) and can be performed efficiently at theedge—which often is also the location where the input data for the ML orAI inferencing originates and is collected. Accordingly, performinginference at the edge provides the benefit of better latency (e.g.,lower latency, higher frame rate, lower response time, larger samplingfrequency), as the input data does not need to first transit over to acloud region prior to inference. In some aspects, trained ML or AImodels (generated in the cloud or on-premises) can be optimized andcompressed significantly prior to delivery or distribution to some (orall) of the edge locations, ultimately enabling the trained model to bedistributed to a greater quantity and range of edge locations, and in amore efficient manner.

In one illustrative example, the systems and techniques described hereincan be used to implement a hub-and-spoke architecture for efficientML/AI inferencing at the edge, based on training (orretraining/finetuning) performed at a centralized cloud or on-premiseslocation. The hub-and-spoke architecture can be orchestrated and managedusing the presently disclosed edge ML/AI monitoring and managementplatform. In some embodiments, a continuous feedback loop can be used tocapture data locally (e.g., at the containerized edge compute unitsmanaged by the edge ML/AI monitoring and management platform), performinference, and respond locally. In some aspects, inference resultsand/or features from the source data can be compressed and transmittedfrom the edge to the ML/AI monitoring platform. For instance, thecontainerized edge compute units can be used to compress and transmitinference results and/or source data features back to the presentlydisclosed ML/AI monitoring platform (and/or other centralized managementlocation). Training and finetuning can be performed in the cloud orusing a centralized on-premises infrastructure, in both cases withtraining or finetuning operations mediated and managed by the edge ML/AImonitoring platform. The edge ML/AI monitoring platform can subsequentlytransmit or otherwise distribute the new or updated ML and AI modelsback to the edge (e.g., back to some or all of the edge compute unitsincluded in a fleet of edge compute units managed by the presentlydisclosed edge ML/AI monitoring and management platform). In someaspects, the edge ML/AI monitoring and management platform can beconfigured to optimize the usage of cloud, edge, and bandwidth resourcesfor performing ML and/or AI workloads at the edge. The edge ML/AImonitoring and management platform can be further configured to ensureprivacy and security of data generated at the edge (e.g., financialrecords and transactions, personal identifiable information, protectedhealth information, proprietary images and videos, etc.).

Further details regarding the systems and techniques described hereinwill be discussed below with respect to the figures.

FIG. 1 shows a block diagram of a design of a base station 102 and a UE104 that enable transmission and processing of signals exchanged betweenthe UE and the base station, in accordance with some aspects of thepresent disclosure. Design 100 includes components of a base station 102and a UE 104. In some examples, the architecture of base station 102 canbe the same as or similar to an architecture used to implement asatellite constellation ground station (e.g., internet gateway forproviding internet connectivity via a satellite constellation). In someexamples, the architecture of base station 102 can be the same as orsimilar to an architecture used to implement a satellite of a satelliteconstellation and/or a network entity in communication with a satelliteconstellation (e.g., such as the satellite constellations and/ornetworks depicted in FIGS. 2 and 3 ).

As illustrated in FIG. 1 , base station 102 may be equipped with Tantennas 134 a through 134 t, and UE 104 may be equipped with R antennas152 a through 152 r, where in general T≥1 and R≥1. At base station 102,a transmit processor 120 may receive data from a data source 112 for oneor more UEs, select one or more modulation and coding schemes (MCS) foreach UE based at least in part on channel quality indicators (CQIs)received from the UE, process (e.g., encode and modulate) the data foreach UE based at least in part on the MC S(s) selected for the UE, andprovide data symbols for all UEs. Transmit processor 120 may alsoprocess system information (e.g., for semi-static resource partitioninginformation (SRPI) and/or the like) and control information (e.g., CQIrequests, grants, upper layer signaling, and/or the like) and provideoverhead symbols and control symbols. Transmit processor 120 may alsogenerate reference symbols for reference signals (e.g., thecell-specific reference signal (CRS)) and synchronization signals (e.g.,the primary synchronization signal (PSS) and secondary synchronizationsignal (SSS))). A transmit (TX) multiple-input multiple-output (MIMO)processor 130 may perform spatial processing (e.g., precoding) on thedata symbols, the control symbols, the overhead symbols, and/or thereference symbols, if applicable, and may provide T output symbolstreams to T modulators (MODs) 132 a through 132 t. The modulators 132 athrough 132 t are shown as a combined modulator-demodulator (MOD-DEMOD).In some cases, the modulators and demodulators can be separatecomponents. Each modulator of the modulators 132 a to 132 t may processa respective output symbol stream, e.g., for an orthogonalfrequency-division multiplexing (OFDM) scheme and/or the like, to obtainan output sample stream. Each modulator of the modulators 132 a to 132 tmay further process (e.g., convert to analog, amplify, filter, andupconvert) the output sample stream to obtain a downlink signal. Tdownlink signals may be transmitted from modulators 132 a to 132 t via Tantennas 134 a through 134 t, respectively. According to certain aspectsdescribed in more detail below, the synchronization signals can begenerated with location encoding to convey additional information.

At UE 104, antennas 152 a through 152 r may receive the downlink signalsfrom base station 102 and/or other base stations and may providereceived signals to demodulators (DEMODs) 154 a through 154 r,respectively. The demodulators 154 a through 154 r are shown as acombined modulator-demodulator (MOD-DEMOD). In some cases, themodulators and demodulators can be separate components. Each demodulatorof the demodulators 154 a through 154 r may condition (e.g., filter,amplify, downconvert, and digitize) a received signal to obtain inputsamples. Each demodulator of the demodulators 154 a through 154 r mayfurther process the input samples (e.g., for OFDM and/or the like) toobtain received symbols. A MIMO detector 156 may obtain received symbolsfrom all R demodulators 154 a through 154 r, perform MIMO detection onthe received symbols if applicable, and provide detected symbols. Areceive processor 158 may process (e.g., demodulate and decode) thedetected symbols, provide decoded data for UE 104 to a data sink 160,and provide decoded control information and system information to acontroller/processor 180. A channel processor may determine referencesignal received power (RSRP), received signal strength indicator (RSSI),reference signal received quality (RSRQ), channel quality indicator(CQI), and/or the like.

On the uplink, at UE 104, a transmit processor 164 may receive andprocess data from a data source 162 and control information (e.g., forreports comprising RSRP, RSSI, RSRQ, CQI, and/or the like) fromcontroller/processor 180. Transmit processor 164 may also generatereference symbols for one or more reference signals (e.g., based atleast in part on a beta value or a set of beta values associated withthe one or more reference signals). The symbols from transmit processor164 may be precoded by a TX-MIMO processor 166 if application, furtherprocessed by modulators 154 a through 154 r (e.g., for DFT-s-OFDM,CP-OFDM, and/or the like), and transmitted to base station 102. At basestation 102, the uplink signals from UE 104 and other UEs may bereceived by antennas 134 a through 134 t, processed by demodulators 132a through 132 t, detected by a MIMO detector 136 if applicable, andfurther processed by a receive processor 138 to obtain decoded data andcontrol information sent by UE 104. Receive processor 138 may providethe decoded data to a data sink 139 and the decoded control informationto controller (e.g., processor) 140. Base station 102 may includecommunication unit 144 and communicate to a network controller 131 viacommunication unit 144. Network controller 131 may include communicationunit 194, controller/processor 190, and memory 192. In some aspects, oneor more components of UE 104 may be included in a housing. Memories 142and 182 may store data and program codes for the base station 102 andthe UE 104, respectively. A scheduler 146 may schedule UEs for datatransmission on the downlink, uplink, and/or sidelink.

Data Network Connectivity Using Satellite Constellations

As noted previously, low-orbit satellite constellation systems have beenrapidly developed and deployed to provide wireless communications anddata network connectivity. A fleet of discrete satellites (also referredto as “birds”) can be arranged as a global satellite constellation thatprovides at least periodic or intermittent coverage to a large portionof the Earth's surface. In many cases, at least certain areas of theEarth's service may have continuous or near-continuous coverage from atleast one bird of the satellite constellation. For instance, a globalsatellite constellation can be formed based on a stable (and thereforepredictable) space geometric configuration, in which the fleet of birdsmaintain fixed space-time relationships with one another. A satelliteconstellation be used to provide data network connectivity toground-based devices and/or other terrestrial receivers. For example, asatellite constellation can be integrated with or otherwise provideconnectivity to one or more terrestrial (e.g., on-ground) data networks,such as the internet, a 4G/LTE network, and/or a 5G/NR network, amongvarious others. In one illustrative example, a satellite internetconstellation system can include a plurality of discrete satellitesarranged in a low-earth orbit and used to provide data networkconnectivity to the internet.

To implement an internet satellite constellation, the discretesatellites can be used as space-based communication nodes that coupleterrestrial devices to terrestrial internet gateways. The terrestrialinternet gateways may also be referred to as ground stations, and areused to provide connectivity to the internet backbone. For instance, agiven satellite can provide a first communication link to a terrestrialdevice and a second communication link to a ground station that isconnected to an internet service provider (ISP). The terrestrial devicecan transmit data and/or data requests to the satellite over the firstcommunication link, with the satellite subsequently forwarding thetransmission to the ground station internet gateway (from which pointonward the transmission from the device is handled as a normal internettransmission). The terrestrial device can receive data and/or requestsusing the reverse process, in which the satellite receives atransmission from the ground station internet gateway via the secondcommunication link and then forwards the transmission to the terrestrialdevice using the first communication link.

Although an internet satellite constellation includes a fleet ofdiscrete satellites, in some cases terrestrial devices connected with asatellite may communicate with a ground station/internet gateway that isalso able to communicate with the same satellite. In other words, it istypically the case that the first and second communication linksdescribed above must be established with the same satellite of thesatellite constellation. A user connecting to any particular satelliteis therefore limited by the ground station/internet gateways that arevisible to that particular satellite. For instance, a user connected toa satellite that is unable to establish a communication link with aground station/internet gateway is therefore unable to connect to theinternet—although the fleet of satellites is a global network in termsof spatial diversity and arrangement, the individual satellites functionas standalone internet relay nodes unless an inter-satellite linkcapability is provided.

In some cases, inter-satellite links can allow point to pointcommunications between the individual satellites included in a satelliteconstellation. For instance, data can travel at the speed of light fromone satellite to another, resulting in a fully interconnected globalmesh network that allows access to the internet as long as theterrestrial device can establish communication with at least onesatellite of the satellite internet constellation. In one illustrativeexample, a satellite internet constellation can implementinter-satellite links as optical communication links. For example,optical space lasers can be used to implement optical intersatellitelinks (ISLs) between some (or all) of the individual birds of asatellite constellation. In this manner, the satellite internetconstellation can be used to transmit data without the use of localground stations, and may be seen to provide truly global coverage.

For instance, optical laser links between individual satellites in asatellite constellation can reduce long-distance latency by as much as50%. Additionally, optical laser links (e.g., ISLs) can enable the moreefficient sharing of capacity by utilizing the otherwise wastedsatellite capacity over regions without ground station internetgateways. Moreover, optical laser links allow the satelliteconstellation to provide internet service (or other data networkconnectivity) to areas where ground stations are not present and/or areimpossible to install.

To implement a satellite constellation, one or more satellites may beintegrated with the terrestrial infrastructure of a wirelesscommunication system. In general, satellites may refer to Low EarthOrbit (LEO) devices, Medium Earth Orbit (MEO) devices, GeostationaryEarth Orbit (GEO) devices, and/or Highly Elliptical Orbit (HEO) devices.In some aspects, a satellite constellation can be included in or used toimplement a non-terrestrial network (NTN). A non-terrestrial network(NTN) may refer to a network, or a segment of a network, that uses anairborne or spaceborne vehicle for transmission. For instance,spaceborne vehicles can refer to various ones of the satellitesdescribed above. An airborne vehicle may refer to High AltitudePlatforms (HAPs) including Unmanned Aircraft Systems (UAS). An NTN maybe configured to help to provide wireless communication in un-served orunderserved areas to upgrade the performance of terrestrial networks.For example, a communication satellite (e.g., of a satelliteconstellation) may provide coverage to a larger geographic region than aterrestrial network base station. The NTN may also reinforce servicereliability by providing service continuity for UEs or for movingplatforms (e.g., passenger vehicles-aircraft, ships, high speed trains,buses). The NTN may also increase service availability, includingcritical communications. The NTN may also enable network scalabilitythrough the provision of efficient multicast/broadcast resources fordata delivery towards the network edges or even directly to the userequipment.

FIG. 2 is a diagram illustrating an example configuration 200 of an NTNfor providing data network connectivity to terrestrial (ground-based)devices. In one illustrative example, the NTN can be a satelliteinternet constellation, although various other NTNs and/or satelliteconstellation data network connectivity types may also be utilizedwithout departing from the scope of the present disclosure. As usedherein, the terms “NTN” and “satellite constellation” may be usedinterchangeably.

An NTN may refer to a network, or a segment of a network, that uses RFresources on-board an NTN platform. The NTN platform may refer to aspaceborne vehicle or an airborne vehicle. Spaceborne vehicles includecommunication satellites that may be classified based on their orbits.For example, a communication satellite may include a GEO device thatappears stationary with respect to the Earth. As such, a single GEOdevice may provide coverage to a geographic coverage area. In otherexamples, a communication satellite may include a non-GEO device, suchas an LEO device, an MEO device, or an HEO device. Non-GEO devices donot appear stationary with respect to the Earth. As such, a satelliteconstellation (e.g., one or more satellites) may be configured toprovide coverage to the geographic coverage area. An airborne vehiclemay refer to a system encompassing Tethered UAS (TUA), Lighter Than AirUAS (LTA), Heavier Than Air UAS (HTA) (e.g., in altitudes typicallybetween 8 and 50 km including High Altitude Platforms (HAPs)).

A satellite constellation can include a plurality of satellites, such asthe satellites 202, 204, and 206 depicted in FIG. 2 . The plurality ofsatellites can include satellites that are the same as one anotherand/or can include satellites that are different from one another. Aterrestrial gateway 208 can be used to provide data connectivity to adata network 210. For instance, the terrestrial gateway 208 can be aground station (e.g., internet gateway) for providing data connectivityto the internet. Also depicted in FIG. 2 is a UE 230 located on thesurface of the earth, within a cell coverage area of the first satellite202. In some aspects, the UE 230 can include various devices capable ofconnecting to the NTN 200 and/or the satellite constellation thereof forwireless communication.

The gateway 208 may be included in one or more terrestrial gateways thatare used to connect the NTN 200 and/or satellite constellation thereofto a public data network such as the internet. In some examples, thegateway 208 may support functions to forward a signal from the satelliteconstellation to a Uu interface, such as an NR-Uu interface. In otherexamples, the gateway 208 may provide a transport network layer node,and may support various transport protocols, such as those associatedwith providing an IP router functionality. A satellite radio interface(SRI) may provide IP trunk connections between the gateway 208 andvarious satellites (e.g., satellites 202-206) to transport NG or F1interfaces, respectively.

Satellites within the satellite constellation that are within connectionrange of the gateway 208 (e.g., within line-of-sight of, etc.) may befed by the gateway 208. The individual satellites of the satelliteconstellation can be deployed across a satellite-targeted coverage area,which can correspond to regional, continental, or even global coverage.The satellites of the satellite constellation may be served successivelyby one or more gateways at a time. The NTN 200 associated with thesatellite constellation can be configured to provide service and feederlink continuity between the successive serving gateways 208 with timeduration to perform mobility anchoring and handover.

In one illustrative example, the first satellite 202 may communicatewith the data network 210 (e.g., the internet) through a feeder link 212established between the first satellite 202 and the gateway 208. Thefeeder link 212 can be used to provide bidirectional communicationsbetween the first satellite 202 and the internet backbone coupled to orotherwise provided by gateway 208. The first satellite 202 cancommunicate with the UE 230 using a service link 214 established withinthe cell coverage (e.g., field-of-view) area of an NTN cell 220. The NTNcell 220 corresponds to the first satellite 202. In particular, thefirst satellite 202 and/or service link 214 can be used to communicatewith different devices or UEs that are located within the correspondingNTN cell 220 of first satellite 202.

More generally, a feeder link (such as feeder link 212) may refer to awireless link between a gateway and a particular satellite of asatellite constellation. A service link (such as service link 214) mayrefer to a wireless link between a UE and particular satellite of asatellite constellation. In some examples, one or more (or all) of thesatellites of a satellite constellation can use one or more directionalbeams (e.g., beamforming) to communicate with the UE 230 via servicelink 214 and/or to communicate with the ground station/internet gateway208 via feeder link 212. For instance, the first satellite 202 may usedirectional beams (beamforming) to communicate with UE 230 via servicelink 214 and/or to communicate with gateway 208 via feeder link 212. Abeam may refer to a wireless communication beam generated by an antennaon-board a satellite.

In some examples, the UE 230 may communicate with the first satellite202 via the service link 214, as described above. Rather than the firstsatellite 202 then using the feeder link 212 to forward the UEcommunications to internet gateway 208, the first satellite 202 mayinstead relay the communication to second satellite 204 through aninter-satellite link (ISL) 216. The second satellite 204 cansubsequently communicate with the data network 210 (e.g., internet)through a feeder link 212 established between the second satellite 204and the internet gateway 208. In some aspects, the ISL links can beprovided between a constellation of satellites and may involve the useof transparent payloads on-board the satellites. The ISL link mayoperate in an RF frequency or an optical band. In one illustrativeexample, the ISL links between satellites of a satellite constellationcan be implemented as optical laser links (e.g., using optical spacelaser transceivers provided on the satellites), as was noted previouslyabove.

In the illustrated example of FIG. 2 , the first satellite 202 mayprovide the NTN cell 220 with a first physical cell ID (PCI). In someexamples, a constellation of satellites may provide coverage to the NTNcell 220. For example, the first satellite 202 may include a non-GEOdevice that does not appear stationary with respect to the Earth. Forinstance, the first satellite 202 can be a low-earth orbit (LEO)satellite included in a LEO satellite constellation for providing datanetwork connectivity. As such, a satellite constellation (e.g., one ormore satellites) may be configured to provide coverage to the NTN cell220. For example, the first satellite 202, second satellite 204, andthird satellite 206 may be part of a satellite constellation thatprovides coverage to the NTN cell 220.

In some examples, satellite constellation deployment may providedifferent services based on the type of payload onboard thesatellite(s). The type of payload may determine whether the satelliteacts as a relay node or a base station. For example, a transparentpayload is associated with the satellite acting as a relay node, while anon-transparent payload is associated with the satellite acting as abase station. A transparent payload may implement frequency conversionand a radio frequency (RF) amplifier in both uplink (UL) and downlink(DL) directions and may correspond to an analog RF repeater. Atransparent payload, for example, may receive UL signals from all servedUEs and may redirect the combined signals DL to an earth station (e.g.,internet gateway 208) without demodulating or decoding the signals.Similarly, a transparent payload may receive an UL signal from an earthstation and redirect the signal DL to served UEs without demodulating ordecoding the signal. However, the transparent payload may frequencyconvert received signals and may amplify and/or filter received signalsbefore transmitting the signals.

A non-transparent payload may receive UL signals and demodulate ordecode the UL signal before generating a DL signal. For instance, thefirst satellite 202 may receive UL signals from one or more served UEs(e.g., within the cell 220) and subsequently demodulate or decode the ULsignals prior to generating one or more corresponding DL signals to theinternet gateway 208. Similarly, the first satellite 202 may receive ULsignals from the internet gateway 208 and subsequently demodulate ordecode the UL signals prior to generating one or more corresponding DLsignals to the served UEs within cell 220.

Satellite Internet Constellations

A satellite internet constellation is a fleet of satellite internetconstellation satellites (also referred to as “birds”) arranged in alow-earth orbit (LEO). Satellite internet constellations can beimplemented based on the idea that, with a sufficiently largeconstellation, at any given time at least one satellite should besufficiently close to communicate with both a user satellite dish and asatellite dish at an internet gateway. In such implementations, theinternet gateway satellite dish is typically located in the same generalvicinity (e.g., geographic area) as the user satellite dish because, asnoted previously above, the same satellite is used to communicate withboth the internet gateway and the user. Based on the same satellitecommunicating with both the user and the internet gateway, the satellitecan be used to route (e.g., relay) internet traffic between the customerand the internet via the internet gateway.

Advantageously, users of such satellite internet constellations canconnect to the internet without the requirement of having a physicalconnection to the internet gateway (although it is noted that thedescription herein may be applied equally to standalone satelliteinternet connectivity and/or satellite internet connectivity that iscombined with other connectivity means such as WiFi/wireless, cellular,fiber optic and other wired connections, etc.) Satellite internet usersare typically connected to an internet gateway via a series ofintermediate connections (also referred to as hops). In many cases, thedirect physical connections between internet users and internet gatewaysare provided via internet service providers (ISPs), for example overfiber optic cables or copper lines. Satellite internet constellations(and the associated satellite internet service thereof) can be valuablefor users for whom direct physical connections to an internet gatewayare unavailable or otherwise prohibitively expensive. For instance, insome cases, users in rural or low density areas may not have access tothe internet and/or may not have access to high-speed (e.g., fiber)internet because the cost of a ground-based physical connection to agateway cannot be amortized over a sufficiently large quantity of usersto justify the expense (e.g., as physical internet infrastructure isoften built out by ISPs with the expectation of recouping the buildoutcost via monthly internet service fees charged to its customers).

Satellite internet constellations and the associated satellite internetservice (also referred to as “satellite internet connectivity” or“satellite connectivity”) can also be valuable as a backup or secondarycommunication link. For instance, satellite connectivity can be used toaugment communications performed over a direct physical connections suchas fiber, with a portion of communications routed over a fiber link anda portion of communications routed over a satellite connectivity link.The satellite connectivity link can be configured as a secondary link, aprimary link, etc. The satellite connectivity link can additionally, oralternatively, be configured as a backup link for communicationsfailover or fallback in case of a degradation or other interruption to aprimary communication link (e.g., a primary finer link, etc.).

Satellite internet constellations can provide internet access to bothusers who are adequately served by conventional/existing physicalground-based internet connections and to users who are not adequatelyserved (if served at all) by the existing physical ground-based internetconnections. In some cases, geographic considerations beyond populationdensity can also be an impediment to providing ground-based internetconnectivity. For instance, island or archipelago geographies may bedensely populated but have a landmass that is spread across numerousislands—in this case, it is logistically challenging and financiallycumbersome to run fiber connections to all of the islands. Accordingly,geographic considerations can also act as a barrier to usingconventional ground-based physical connections between users andinternet gateways.

FIG. 3 is a diagram illustrating an example of a satellite internetconstellation network 300, which in some aspects can be used to providelow latency satellite internet connectivity to a plurality of users. Theplurality of users can be associated with a corresponding plurality ofUEs, such as the UE 330 depicted in FIG. 3 . The UE(s) 330 can includevarious different computing devices and/or networking devices. In someembodiments, the UEs 330 can include any electronic device capable ofconnecting to a data network such as the internet.

The UE 330 can be associated with a plurality of client-side satelliteinternet constellation dishes, shown here as the satellite dishes 312,314, and 316, although it is noted that a greater or lesser quantity ofsatellite dishes can be used without departing from the scope of thedisclosure. In one illustrative example, the UE 330 and the satellitedishes 312, 314, 316 can be associated with one another based on acommon or proximate geographic location, area, region, etc. In otherwords, it is contemplated that a plurality of client-side satelliteinternet constellation dishes can be deployed to serve (e.g., provideconnectivity to the satellite internet constellation) various differentgeographic areas, with various granularities as desired. For example, agroup of satellite dishes can be deployed in and around a city, a town,a region, etc. The groups of satellite dishes can also be deployed inrural areas (e.g., lower-density concentrations of users). Multiplesatellite dishes may be connected the same Edge Compute Unit to offerredundancy and resilience against outage, high latency, or lowbandwidth.

In some cases, one or more satellite dishes (and/or groups thereof) canbe deployed in remote areas that are distant from population centers,and in particular, that are distant from various types of infrastructure(e.g., including but not limited to electrical/power connectivity,internet and/or communication networking, compute capacity, reach ofskilled personnel, access to road transportation, etc.).

The client-side satellite dishes 312, 314, 316 can communicate with asatellite internet constellation, shown in FIG. 3 as including a firstsatellite 302, a second satellite 304, a third satellite 306, and afourth satellite 304. However, it is noted that a greater quantity ofsatellites can be used to implement the satellite internetconstellation, with FIG. 3 presenting a simplified example for purposesof clarity of explanation.

Similarly, a plurality of server-side satellite internet constellationdishes 321, 323, 325 can be provided in association with variousdifferent gateways, such as the gateway 340 depicted in FIG. 3 . In someembodiments, the gateway 340 can be an internet gateway that providesconnectivity to an internet backbone. In some aspects, the gateway 340can be a data center or CDN that caches, hosts, stores, serves, orotherwise provides web content in response to receiving correspondingclient requests for the content. It is again noted that a greater orlesser quantity of server-side satellite dishes can be utilized withoutdeparting from the scope of the present disclosure. As was describedabove with respect to the client-side satellite dishes 312, 314, 316,the server-side satellite dishes 321, 323, 325 can be associated to arespective data center 340 based on a common or proximate geographiclocation, area, region, etc. In one illustrative example, theserver-side satellite dishes 321, 323, 325 can be located at varyinglevels of proximity to the respective data center 340. For instance, aninner layer of server-side satellite dishes can include the satellitedishes 323 and 325, which may be provided at the closest physicaldistance to the data center 340. An outer layer of server-side satellitedishes can include at least the satellite dish 321, which is located ata greater distance away from the data center 340 relative to the innerlayer dishes 323 and 325. In some embodiments, the outer layer satellitedishes can be communicatively coupled to the inner layer satellitedishes via a wired and/or wireless connection. For example, the outerlayer server-side satellite dish 321 can be communicatively coupled tothe inner layer server-side satellite dish 323 via a wireless microwaverelay connection (among various other wireless/RF connections) and/orcan be communicatively coupled to the inner layer server-side satellitedish 323 via a wired fiber connection.

By providing multiple different satellite dishes for communicating withthe satellite internet constellation, at both the client-side associatedwith UE 330 and the server-side associated with datacenter 340, thesystems and techniques described herein can increase the satelliteconstellation ground coverage area available to the UE 330 and to thedatacenter 340. For instance, at the client-side associated with UE 330,the number of birds that are visible to or overhead the set of dishes312, 314, 316 will almost always be greater than the number of birdsthat are visible to or otherwise overhead any individual one of thethree client-side dishes 312, 314, 316. Similarly, at the server-sideassociated with datacenter 340, the number of birds that are visible toor otherwise overhead the set of the three dishes 321, 323, 325 willalmost always be greater than the number of birds that are visible to orotherwise overhead any individual one of the three server-side dishes321, 323, 325.

The interconnecting of the satellite dishes at each respective clientlocation and at each respective server location, when combined with asatellite internet constellation implement optical space lasers or otherISLs, can enable more direct connectivity between the UE 330 and thedatacenter 340. For instance, the UE 330 may use satellite dish 312 tocommunicate with satellite 302, via a service link 352. As illustrated,satellite 302 is out of range of the data center 340 (e.g., satellite302 cannot establish a feeder link with any of the server-side dishes321, 323, 325). In a conventional satellite internet constellationwithout ISLs, UE 330 would therefore be unable to use satellite 302 toobtain internet connectivity with data center 340 (based on therequirement in conventional satellite internet constellations that thesame bird be used to connect the UE and an internet gateway).

Here, however, the UE 330 is able to establish internet connectivitywith datacenter 340 via a first ISL 362 a between satellite 302 andsatellite 304, a second ISL 362 b between satellite 304 and satellite308, and a feeder link from satellite 308 to the server-side satellitedish 323. Notably, the UE 330 can establish internet connectivity withdata center 340 via multiple different ISL-based paths through onedifferent sets of birds of the satellite internet constellation. Forinstance, a first path from UE 330 to datacenter 340 is the combinedpath 352-362 a-362 b-372 described above. At least a second path from UE330 to datacenter 340 may also be utilized. For example, the server-sidedish 316 can communicate with satellite 304 via a service link 354,satellite 304 can communicate with satellite 306 via ISL 364, andsatellite 306 can communicate with server-side dish 321 via feeder link374.

Various other paths from the UE 330 to the datacenter 340 can also beutilized, with the two example paths of FIG. 3 provided for purposes ofexample and illustration, and not intended as limiting. For instance,the UE 330 can establish internet connectivity with datacenter 340 usinga combination of: a particular service link selected from a plurality ofavailable service links between one of the client-side dishes 312, 314,316 to one of the birds of the constellation; one or more particularISLs selected from a plurality of available ISLs between variouscombinations of two or more birds of the constellation; and a particularfeeder link selected from a plurality of available feeder links betweenone of the birds of the constellation to one of the server-side dishes321, 323, 325.

In some embodiments, the plurality of server-side satellite dishes(e.g., the dishes 321, 323, 325) can be located proximate to adatacenter, CDN, or other server-side proxy that serves internet contentdirectly. In this example, the number of hops needed to provide internetconnectivity to the UE 330 can be approximately equal to the 2+thenumber of ISLs in the path through the satellite constellation (e.g., 1xservice link from UE 330 to the constellation, 1x feeder link from theconstellation to the datacenter 340, and any ISLs taken between theservice link satellite and the feeder link satellite).

In another example, the plurality of server-side satellite dishes (e.g.,dishes 321, 323, 325) can be located proximate to a terrestrial internetgateway that connects via ground-based connections, such as fiber, tothe corresponding datacenter, CDN, server-side proxy, etc., that hostscontent requested by UE 330. For instance, one or more server-sidesatellite dishes can be provided proximate to multiple differentterrestrial internet gateways. In this manner, the satellite internetconstellation may, in some cases, analyze a client request from UE 330to determine a particular terrestrial internet gateway that has thelowest latency to a proxy of the web server associated with the clientrequest. Based on the analysis, the satellite internet constellation candetermine one or more ISLs to route the client request to a bird that isoverhead the identified gateway having the lowest latency to the proxy.In some examples, the satellite internet constellation can determine thelowest latency as the lowest latency from one of the terrestrialinternet gateways to a proxy of the requested web server (e.g., withoutaccounting for additional latency introduced by the number of ISLs orinter-satellite constellation hops needed to connect UE 330 to thelowest latency internet gateway). In other example, the satelliteinternet constellation can determine the lowest latency as beinginclusive of both the latency through the ISL hops within the satelliteconstellation plus the latency through the one or more hops from agateway to the proxy.

Notably, the systems and techniques described herein can be used toprovide lower latency satellite internet by decoupling UE 330 from thelimitation of only being able to connect to its local internet gateways.In some cases, the satellite internet constellation can receivesignaling from one or more server-side proxies indicative of a currentload, predicted load, etc., associated with each respective one of theserver-side proxies. Based on the indicated load information for theproxies, the satellite internet constellation can more intelligentlyroute internet traffic to gateways with proxies having sufficientcapacity (and/or the most available capacity) to handle the traffic. Forinstance, the traffic-aware routing (e.g., load balancing) can beimplemented in combination with the latency-based routing describedabove.

In some embodiments, the satellite internet constellation can beconfigured to inspect and/or analyze the contents of internet trafficfrom UE 330. For instance, if the satellite internet constellation isable to inspect the contents of client-side internet traffic, a webclient (e.g., browser) and/or a satellite internet constellationclient-side proxy can maintain a consistent/persistent secure connectionwith an appropriate gateway proxy, thereby reducing the number ofroundtrips by approximately 60%. The roundtrip reduction of 60% may bein addition to the already reduced number of hops between the UE 330 andthe datacenter 340.

Example Embodiments

As noted previously above, the use of edge computing has becomeincreasingly popular for implementing a diverse range of AI and MLapplications, as well as for serving other use cases that demandreal-time processing, minimal latency, high availability, and highreliability. For example, such applications and use cases may rely onhigh-bandwidth sensors that have the ability to generate data at massiverates (e.g., on the order of 50 Gbit/sec or 22 TB/hr), and may beunsuitable for deployments based on the conventional edge-cloud-edgedata transport paradigm.

Example Artificial Intelligence (AI) and Machine Learning (ML) Workloadsat the Edge

Various AI and ML applications (also referred to as workloads,workflows, tasks, etc.) can benefit from edge computing or otherwisebeing implemented at the edge. Edge computing can play an important rolein providing a wide range of AI and ML applications, including (but notlimited to) for use cases that utilize real-time processing, highreliability, high availability, and minimal latency—all of which arefeatures of edge computing and the edge compute units described herein.

For example, edge-deployed AI and ML applications may make heavy use ofone or more high-bandwidth sensors (e.g., such as high-speed and/or HDcameras, stereo cameras, lidar cameras and/or sensor systems,accelerometers and other inertial sensor packages, fiber optic sensor,radar, ultrasonic sensors, etc.). Additionally, multi-modal sensorpackages may include multiple sensors operating over multiple differentmodalities and/or sensor domains. These multi-modal sensor packages cangenerate and/or stream data at rates that can exceed 50 Gbit/s (e.g., 22TB/hr).

Sensor data streams, either high-bandwidth or otherwise, can be providedto one or more AI or ML models that are configured (e.g., trained) toprocess such sensor data for purposes such as real-time decision makingand analytics (among various other purposes). For instance, ML and AImodels that may be associated with ingesting and/or processing massiveor high-bandwidth data streams can include, but are not limited to, deepneural networks (DNNs), convolutional neural networks (CNNs),region-based CNNs (R-CNNs), recurrent neural networks (RNNs), longshort-term memory (LSTM) networks, vision transformers (ViTs),variational autoencoders (VAEs), generative adversarial networks (GANs),autoencoders, transformers, bidirectional encoder representations fromtransformers (BERT), stable diffusion, attention mechanisms, and/orlarge language models (LLMs), etc.

Processing high-bandwidth and other large data streams with an AI or MLmodel can require significant computational power, in some cases on theorder of thousands of teraflops (TFLOPS). One teraflop represents onemillion million (10¹²) floating-point operations per second.

As both the size and complexity of ML and AI models has increased, sotoo has the ability to deploy increasing numbers of increasingly highbandwidth sensors/sensor packages to generate the input data forinference using the ML and AI models. As such, there is an increasingneed for systems and techniques that can be used to deploy and/orimplement high-performance compute nodes (e.g., for running inferenceusing trained AI or ML models) near the sources of sensor and otherinput data.

As the number of interconnected sensors and the corresponding volume ofgenerated data continue to increase, the significance of edge computingbecomes increasingly critical. In particular, it is observed that edgecomputing may act as an enabler for the evolution of intelligentapplications and services (e.g., AI and/or ML models and workloads) thatcan be used to autonomously and continually learn, predict, and adaptusing massive streams of unstructured data. For instance, by 2025, it isprojected that the global data volume will reach 175 zettabytes (175billion TB), with approximately 80% of this data being in anunstructured form.

Unstructured data and datasets have historically been underutilized—forexample, it is estimated that on the order of 90% of unstructureddatasets currently remain unanalyzed and unused. It is additionallynoted that many of these unstructured datasets do not strictly requirestorage in their raw form, which may lack meaningful information.Nevertheless, many unstructured datasets exist, and can be challengingto integrate with existing computational workflows and/or pipelines,which are largely structured and designed for processing at leastpartially structured data. For example, even if all of the unstructureddatasets were to be transferred to the cloud, conventional and existingcloud-based infrastructure is primarily configured to process data inbatches—resulting in considerable time delays between data creation andthe generation of corresponding insights or decisions based on the data.

In one illustrative example, a typical offshore drilling platformproduces a substantial amount of data, ranging from 2-4 TB per day. Ofthis 2-4 TB of raw data generated each day, approximately 80% may remainunused (e.g., 1.6-3.2 TB/day). Even a solitary oil rig operatingremotely in a northern environment can generate over 1 TB of data perday, and in this example, with less than 1% of that data being utilizedfor analytical and/or decision-making purposes. The challenges ofgenerated data volume can become exponentially more difficult with theremoteness of the operating environment. In particular, the difficultyof managing and utilizing large volumes of generated sensor data canincrease non-linearly with the separation distance from existing datanetworks and other communication infrastructure foruploading/transmitting the generated sensor data.

For instance, sensor data generated in remote operating environmentsoften cannot be transmitted over conventional fiber optic or otherphysical/wired internet communication links, based in large part on thelack of such infrastructure in or near the remote operating environment.Consequently, sensor data generated in remote operating environmentsoften must be transmitted over much slower (and often more expensive)wireless communication links, such as cellular and/or satellitecommunication links.

A satellite communication link with a 25 Mbps upload speed will takeapproximately 90 hours (approximately four straight days) to transmit 1TB of data—meaning that the example oil rig generating 1 TB/day willquickly bottleneck any data upload from the oil rig to a data center.The challenge becomes more profound, and increasingly untenable, as theamount of generated data increases. For instance, a large-scale refinerycan easily generate in excess of 10 TB of raw data each day.

One common type of unstructured data that may be generated on a daily(or other regular) basis is video data captured by cameras local to anoperating site. Video data captured by cameras often falls into thecategory of unstructured data because such video data comprises rawvisual information without a pre-defined structure or format. Theincreased use and availability of high-resolution cameras for tasks suchas video-based monitoring, scene understanding, and/or navigationapplications, etc., has led to a surge in unstructured video datageneration. For instance, a 4K camera capturing 30 frames-per-second(fps) generates 5.4 TB of uncompressed video data within a single hour.

Similar increases in raw data generation can be seen in the context ofautonomous vehicles (AVs) that are each equipped with multiple camerasand sensor packages (e.g., lidar, radar, ultrasonic sensors, IMUs/INSs,GPS, etc.). The data generation rate of a single AV can reach or exceed50 Gbit/sec. In the AV use case, a significant portion of this 50Gbit/sec raw data generation can necessitate local and real-timeprocessing in order to enable low-latency decision making for thenavigation and control of the AV as the AV moves through itsenvironment. Notably, even a single IP camera can make an appreciablecontribution to the overall sensor data firehose describedabove—streaming at rates ranging from 0.01-1.20 Mbit/sec, a single IPcamera can generate anywhere between 5-500 MB of data per hour.

Sensors such as IP cameras are often deployed in large quantities, andconsequently these deployments of seemingly low bandwidth contributorscan have significant impacts when considered as a whole. Consider theexample of a stadium that equips IP cameras as part of an extensivesecurity or monitoring system, with a total deployment count of 900 IPcameras. In just a single hour, the 900 IP camera security system cangenerate half a terabyte (0.5 TB) of video data alone. In the securityand monitoring scenario, the IP camera video data needs to be processedin substantially real-time for purposes such as event logistics, threatdetection, safety monitoring, etc. While it is possible for variousforms of unstructured data (e.g., such as the IP camera video data) tobe indexed and stored for later retrieval, the common use cases for suchunstructured data are often themselves the primary driver of the needfor substantially real-time processing and analytical capabilities.

Table 1, below, summarizes various example scenarios/use cases for AIand ML applications in the context of edge computing. Also presented inTable 1 are example requirements for respective sensing, bandwidth,compute, and storage corresponding to each example use case (although itis noted that the information of Table 1 is provided for purposes ofillustration and example, and is not intended to be construed aslimiting):

TABLE 1 Example use case scenarios and corresponding computerequirements and parameters for various AI and ML applications at theedge. Industry Sensors Bandwidth Compute (AI/ML) Applications StorageEnergy Fiber optic sensors, 0.2-0.4 40-300 TOPS 2-4 (offshore oilcameras, pressure, Gbit/sec (drilling productivity, fault detection,TB/day drilling) temperature, flow, real-time drilling analytics,ultrasonic sensors GIS/mapping, rig performance analyses, etc.) EnergyFiber optic pressure 0.5-1 40-300 TOPS 5-10 (oil refinery) and strainsensors, Gbit/sec (predictive analytics, equipment TB/day valve, IRcameras, performance and monitoring, oil thermal and quality grading,etc.) electrochemical sensors Logistics, 5-10 cameras, 4-6 2-50 150-800TOPS 1- 22 Agriculture radar, 2-4 lidar, 10- Gbit/sec (navigation,real-time obstacle TB/hour (autonomous 18 ultrasonic, GPS, detection,path and route planning, (~0.8 vehicles) INS/IMU, localization andmapping/SLAM, etc.) TB/hour odometry stored) Media and 500-1200 IP0.6-1.4 80-400 TOPS 270-630 Entertainment cameras Gbit/sec (real-timeobject detection and GB/hour (security camera (panoramic, tracking,activity recognition, event network, AR/VR) fisheye, IR/nightclassification, crowd analyses, etc.) vision, motion detection)Manufacturing Cameras, stereo 0.5-2 50-200 TOPS per workcell/robot0.25-1 (aviation, and depth sensors, Gbit/sec (object detection, barcodescanning, TB/hour warehouse F/T sensors, (per pick and place, sortation,inspection, automation, position and angle workcell manipulation,robotics) sensing, odometry or robot) palletization/depalletization,etc.) Retail and POS, cameras, 0.5-2 30-80 TOPS 0.1-0.5 Supply ChainRFIS, WiFi Gbit/sec (real-time customer analytics, TB/hour (logistics,last positioning, IPS personalization and recommendation, mile,fulfillment) inventory management and replenishment, shrinkage, etc.)Sustainability Energy monitors, 0.1-1 10-40 TOPS 0.5 infrared cameras,Gbit/sec (real-time energy usage monitoring, TB/day temperature andenergy management, distributed current sensors, power generation andstorage, flow meters automated controls, predictive maintenance, etc.)Healthcare Wearable ECG and 0.1-1 20-50 TOPS 0.5 BP monitors, Gbit/sec(real-time health monitoring, TB/day glucometers, personalized care,predictive health biosensors, fitness analytics, privacy and security oftrackers, cameras healthcare apps)

Table 2, below, summarizes various edge ML/AI applications that can bedeployed in the context of various example industries. It is again notedthat the information of Table 2, as with Table 1, is provided forpurposes of illustration and example, and is not intended to beconstrued as limiting:

TABLE 2 Example edge ML/AI applications that can be deployed in thecontext of various example industries and industry use-case impacts.Industry Example ML/AI Edge Applications Industry Use-Case Impact EnergyReal-time monitoring and anomaly Maintain and increase drillingproductivity; (offshore oil detection; predictive and prescriptiveuninterrupted drilling due to preemptive drilling) analytics; assistivevisual inspection; maintenance; reduce time to map and GIS/mapping ofdrilling discover oil and gas sites; assistive and environments. guidedrepair, inspection, and QA. Energy Predictive and prescriptiveanalytics; Oil and chemical quality control; increase (oil refinery)chemical process monitoring; oil productivity and reduce downtime;optimize quality grading; refinery equipment refinery processes andefficiencies, flow inspection; pressure, temperature, and control;detect leaks and gas concentrations; flow monitoring. safety andproactive maintenance schedules. Logistics, Visual inspection anddetection of Automated yard and terminal management; Agriculture defects(rail, locomotive, wheel); yard predictive maintenance; increasecapacity (railroad monitoring and management- utilization; increasesafety and security by transportation) schedule fueling and maintenance,timely interventions; minimize safety risks; container placement andtracking. increase visibility, reliability/predictability oftransportation. Manufacturing Aerodynamic simulation, structural Reducedefects; minimize scrap and rework (aviation stability, and vibrationanalysis, FEA, rates; lower lead times, drive compliance and systems)accelerated life-cycle testing, digital safety certification; quickersimulation and twin modeling, high- resolution testing cycles; improveaerodynamic structural inspection. modeling and validation againstwind-tunnel data. Manufacturing Pick and place; binning/sortation;Increase throughput, inbound and outbound (warehousepalletization/depalletization; inventory efficiencies; lower defectrates and sidelines; automation, control and quality assurance; object24/7 lights-out operation, savings in labor and robotics) detection andmanipulation; package variable costs. scanning and inspection. Media andReal-time object detection and Reduce safety risks and incidents;streamline Entertainment recognition; activity recognition; flow andminimize wait times and crowding; (security crowd monitoring; eventautomated check-ins and just-walk-in-and-out camera classification;biometric and facial technology; incident visibility and forensics;network) recognition. reduce costs and insurance premiums; lowerincident- response time.

As contemplated herein, the disclosed systems and techniques can be usedto provide fleet and asset management for edge computing of various MLand/or AI workloads, including one or more (or all) of the examplespresented above in Table 1. More generally, it is contemplated that thesystems and techniques described herein can be used to provide edgecompute units and management thereof configured for low latency, highavailability, real-time processing, and local operation (e.g., edgeoperation) with minimal dependence on remote, cloud, or on-premisesinfrastructure. Described below are additional details of example ML andAI workloads that can be implemented at the edge according to thepresently disclosed systems and techniques for fleet and assetmanagement for edge computing of ML and AI workloads.

Example 1: Object Detection and Recognition

Object detection and recognition tasks can be performed using various MLand/or AI models, architectures, etc. Common object detection andrecognition tasks can include, but are not limited to, tasks such asidentifying objects, people, or specific events in video streams. Objectdetection and recognition tasks often require (and/or benefit from)real-time or near-real-time response. By performing the processing ofthe underlying input data for the object detection and recognition task(e.g., video or camera data, etc.) at the edge, the processing isperformed closer to the data-generating source (e.g., cameras or othersensors), and the latency of the task can be significantly reduced.Advantageously, reducing the latency of object detection and recognitiontasks can be seen to enable faster decision-making, for instanceallowing for immediate actions and/or alerts to be triggered based ondetected objects (e.g., such as in security systems, AVs, and/orindustrial robot use cases, etc.).

Object detection and recognition tasks are typically performed based onanalyzing large amounts of data, such as video streams orhigh-resolution images (e.g., with a single IP camera capable ofgenerating 500 GB/hour, or more, as described previously above).Transferring object detection and recognition tasks from the cloud orother central location (e.g., on-premises location) to the edge canreduce the latency associated with receiving the object detection andrecognition output at the edge, and can additionally reduce thebandwidth utilized for uplink and downlink communications at the edge.For instance, the use of edge computing allows for local video and imageanalysis, reducing the amount of data that needs to be transmitted andstored to a location that is remote from the edge (e.g., cloud,on-premises location/data center, etc.), and thereby optimizingbandwidth usage while reducing network and storage costs. Edge computingfor object detection and recognition tasks can also be seen to decreasethe conventional dependency on stable and high-bandwidth internetconnections, cloud service availability, and potential latency issuesassociated with cloud-based processing. Moreover, transferring objectdetection and recognition tasks to being implemented and managed at theedge can also be seen to provide a system with increased autonomy andresilience, making such an edge computing system suitable for detectionand tracking applications that require continuous functionality anduninterrupted performance, especially in remote or offline settings.

In one illustrative example, consider an object detection andrecognition task that is performed based on raw video data comprisingsurveillance footage captured by one or more drones. In this example, itis desirable for vehicles within the drone surveillance footage to bedetected and classified by type in real-time. For instance, the vehicleclassification types can include ‘car,’ ‘truck,’ ‘van,’ ‘bus,’‘autorickshaw,’ etc., among various others. Once detected or otherwiseidentified by an input, a selected vehicle can be tracked acrossmultiple video surveillance frames.

This capability of object detection, recognition, and tracking can beextended to include a plurality of additional surveillance cameras in ageographic region (e.g., neighborhood, etc.), where the additionalsurveillance cameras can also be drone-based and/or can be non-dronebased. For instance, the object detection, recognition, and trackingcapabilities can be extended to additional surveillance cameras in thesame neighborhood as the drone-based surveillance camera, includingsurveillance cameras mounted on fixed placements, pan-tilt head cameras,and/or moving platform cameras, etc.

It is important to note that significant portions of the totalsurveillance camera footage captured by the system may lack meaningfulor relevant information—many hours of video surveillance footage caninclude long periods of inactivity, empty scenes, uneventful situations,etc., and may be collectively referred to as “empty frames” or“non-actionable footage.” In many examples, only a small percentage ofthe total captured surveillance video footage (e.g., from 1% to 20%) maycontain events or activities that are relevant to the intendedsurveillance objectives.

In general, the percentage of raw data from surveillance cameras thatcontains meaningful or actionable information relevant to the currentsurveillance task or objective can depend on various factors, which caninclude (but are not limited to) the specific surveillance system, thecamera placement, the scene dynamics, the surveillance purpose, goal, orobjective, etc.

In some aspects, one or more AI or ML models or algorithms can betrained to detect and identify specific objects, events, and/oranomalies, etc., in an input comprising captured surveillance footage.The use of trained AI or ML models for such object detection,recognition, and tracking tasks can reduce the need for manual review ofthe vast amounts of raw surveillance footage, and can enable thesurveillance system to focus on extracting meaningful information andalerting operators (and/or triggering appropriate remediation actions)when relevant events are detected to occur in real-time. Any storedfootage may additionally be indexed for efficient querying andretrieval.

Some object detection applications or use cases (e.g., such as criticalsurveillance systems, smart cameras, etc.) can involve capturingsensitive or private data. In some aspects, performing the correspondingprocessing at the edge can be seen to minimize the need to transmit thissensitive or private data to an external or remote cloud server, therebyreducing privacy concerns while also enhancing data security. In somecases, privacy preservation and local handling of sensitive data can beof particular import in scenarios where strict data regulations and/orprivacy requirements must be met (an increasing occurrence in variousregulatory regimes around the world today).

Example 2: Sensor Integration and Scene Understanding

In some aspects, the use of edge computing (e.g., also referred toherein as “edge computation”) can enable the integration of multiplesensors (e.g., such as cameras, lidar, radar, ultrasonic arrays, stereorigs, range scanners, accelerometers, gyroscopes, and other IoT devices,etc.) in a distributed edge-computing environment. By using one or moreedge compute units to process the respective data from these varioussensors at the edge, it becomes possible to analyze and combine datafrom multimodal data sources in real-time. In some cases, sensorintegration enabled by edge computing can be seen to enhance the overallunderstanding (e.g., analytical understanding and/or human understandingbased on review of the analytical results) of the scene, for instancebased at least in part on the use of the edge computation sensorintegration to provide a more comprehensive and accurate view of thesurrounding environment at the edge.

For example, cameras can be used to provide high-resolution shape,color, and texture information for object detection and recognitiontasks applied to objects such as pedestrians, vehicles, andmotorcyclists. While the resolution of a typical camera is considerablyhigher than that of conventional lidar sensors/systems, the typicalcamera has a limited field of view (FOV) and cannot precisely estimatethe distance between the camera and one or more objects within thecamera's FOV. Image data from a typical camera also cannot usually beused to perform depth estimation or otherwise calculate depthinformation corresponding to an object without being combined with imagedata of the same object, as captured by one or more additional cameras.More generally, depth estimation performed based on one or more camerascan often have a significantly lower precision than depth estimationperformed using lidar (which is a remote sensing technique that useslight in the form of a pulsed laser to measure depth samples). Inaddition, a camera is relatively sensitive to light changes, ambientlighting, and scene reflectance, etc.—artifacts having a less prominentor negligible impact on lidar imaging of the same scene. However, lidarsensors and systems can have difficulty recognizing color andclassifying objects in comparison to camera-based sensors and systems.

In one illustrative example, by using sensor integration, an edgecompute unit (and/or other edge computing device(s) configured foroperation with the presently disclosed systems and techniques for fleetand asset management for AI/ML edge computing) can acquire complementaryinformation and/or sensor data streams corresponding to a surroundingenvironment. For example, the edge compute unit can use sensor data withdifferent characteristics to obtain a more complete or comprehensivecharacterization of the surrounding environment. Multiple sensor datastreams (obtained from multiple different sensors) can also be used toovercome limitations of individual sensors (e.g., such as thelimitations described in the example above with respect to lidar andcamera) and to reduce the uncertainty of individual sensors.Accordingly, use cases such as those relating to autonomous vehicles(AVs), robotics, and/or industrial automation, etc., may benefit fromreal-time sensor integration for safety and reliability. In some cases,real-time sensor integration can enable rapid scene understanding,object detection, and/or object tracking (among various otherapplications) to be implemented at the edge, while further enablingtimely and appropriate actions to be taken based on the multimodalsensor inputs.

In one illustrative example, multimodal sensor inputs and/or sensorintegration and scene understanding can be implemented based onreceiving respective data streams from cameras (e.g., visual lightcameras) and from lidar systems. For instance, respective data streamsfrom one or more cameras and one or more lidars can be integrated on alocomotive and/or associated railway infrastructure, and used to monitorthe railway or track ahead of the locomotive as it moves along therailway. In some examples, deep learning-based sensor integration models(e.g., such as PointNet, PTA-Det, etc.) can be deployed at the edge tocombine raw point cloud data (e.g., derived from lidar data stream(s))with image-based visual features (e.g., derived from image data). Forinstance, the one or more deep learning-based sensor integration modelscan be implemented and/or deployed using one or more of the edge computeunits described herein. Additionally, the one or more edge compute units(and the deep learning-based sensor integration models implementedthereon) can be monitored and managed using the presently disclosedsystems and techniques for fleet and asset management for ML/AI edgecomputing.

In some cases, the one or more deep learning-based sensor integrationmodels deployed at the edge can be configured or used to combine the rawpoint cloud data with image-based visual features in order to detectspecific objects that are relevant to train track safety (e.g., such asfallen trees, rocks, unauthorized personnel on the tracks in the scene,etc.). By analyzing the combined data streams locally (e.g., using oneor more edge compute units), these relevant objects can be identified insubstantially real-time, and can be used to trigger alerts and/or toinitiate appropriate actions to ensure track clearance ahead of thelocomotive. Monitoring the condition of the tracks in real-time (e.g.,using an edge compute unit in the same geographic area as a section oftracks, and/or using an edge compute unit onboard the locomotivetraversing the tracks, etc.) can enable immediate detection of anyobstructions, debris, or hazards that may be present on the tracks.

Sensor integration can also be used to provide additional informationabout the track conditions, including, but not limited to, measurementsof height, depth, curvature and/or any abnormalities that may affecttrain safety. The edge-based implementation of the deep learning-basedsensor integration models (and any other ML or AI models) allows forimmediate remediation action to be taken, such as alerting the trainoperator, signaling maintenance crews, and/or triggering automatedsafety measures, etc. In remote or rural areas with limited networkconnectivity, sensor integration at the edge enables real-timetrain-track monitoring and identification of clear tracks in the sceneahead of the locomotive, even in the absence of a stable internetconnection. In such examples, local processing and real-time sceneanalysis (e.g., implemented using the edge compute unit(s)) can be usedto ensure continuous monitoring and safety measures, regardless of(e.g., independent of) network availability (or lack thereof).

In some cases, given an emergency-braked deceleration of 1.5 m/s2 (0.15g), the braking distance of a locomotive is approximately 260 meters at100 km/h (60 mph) and 580 meters at 150 km/h (90 mph). Currentlyavailable ultra-long-range solid-state lidars can be configured with thecapability to scan objects up to 600 meters away. These lidars provide aresolution of up to 700 lines at frame rates ranging from 1 to 30 fps,offering an angular resolution of 0.01° and a precision of ±2 cm. Suchultra-long range solid-state lidars may be frequently utilized in railprojects for accurate ranging and long-distance detection. With multiplelasers or channels (e.g., in some cases ranging from 8 to 32), theselidars can generate over 2 million data points per second, correspondingto a data generation rate of 32 Mbit/sec (equivalent to 14.4 GB/hrwithout compression). The 3D scans obtained from one or more lidars canbe processed and combined with high-resolution images and odometry atthe edge (e.g., using one or more edge compute units). This enables theprompt identification of potential obstacles, damage, or dangers on thetrain tracks. By leveraging edge computing, these lidar scans can beswiftly analyzed and utilized for enhanced safety measures, without everleaving the edge location where the lidar sensor system is located andwhere the lidar scans were obtained/generated.

Example 3: Facial Recognition and Biometric Identification

In another illustrative example, performing facial recognition andbiometric identification at the edge can be used to enhance privacy andsecurity, of both the processing task itself and relating to theunderlying or constituent data provided as input and used to perform theprocessing task. For instance, when an edge compute unit is used toimplement a facial recognition or biometric identification task (e.g.,performs inference using one or more facial recognition or biometricidentification ML or AI models), the underlying input data will remainon the edge compute unit and its local network. Accordingly, the facialrecognition or biometric identification task can be performed withoutany need to transmit sensitive biometric data to external servers orcloud platforms. This reduces the risk of data breaches and unauthorizedaccess to personal information, ensuring better privacy protection forindividuals. By performing processing locally at the edge, only relevantinformation, and in particular, only relevant output information (e.g.,such as extracted features or identification results) needs to betransmitted off of the local edge compute unit and associated localnetwork. The edge computation approach can be seen to reduce bandwidthconsumption, alleviate network congestion, and save on data transmissioncosts. Moreover, edge processing enables facial recognition andbiometric identification systems to operate even in scenarios withlimited or intermittent network connectivity. The processing algorithmsand models are deployed directly on the edge devices, allowing foroffline operation without relying on constant cloud connectivity. Thisensures continuous functionality and uninterrupted identificationcapabilities, which can be particularly beneficial in remote or offlineenvironments. In some cases, commonly employed biometric identificationmethods include facial, fingerprint, iris, voice, palmprint, retina,vein, signature recognition; gait analysis; body thermal signature;and/or DNA biometrics—each of which may be implemented fully or in parton a local edge compute unit.

For example, consider a railway tunnel that is monitored using one ormore forward looking infrared (FLIR) cameras or FLIR devices. In thisexample, a FLIR camera can capture thermal images (e.g., as still framesor as frames of video data) that can be processed locally at the edge inreal-time for detecting people or other foreign/unauthorized objects inthe railway tunnel. The use of edge computation/edge analysis can alsoenable quick identification (e.g., substantially real-timeidentification) of individuals based on the individual's respectivethermal signature and gait pattern, thereby distinguishing authorizedpersonnel from those who should not be near the tracks.

For instance, a FLIR camera image captured at time ti may include asingle individual, who is tagged and identified as authorized personnel.The authorized individual can be identified and recognized at differenttime instances (e.g., a plurality of time instances, including at thetime instance ti) from their gait and thermal signature patterns. Byprocessing the FLIR data locally at the edge, identification results canbe generated without relying on cloud infrastructure or distantprocessing centers that must first receive the FLIR camera images orother sensitive data transmitted over a public network. In someexamples, the reduction in latency achieved by implementing FLIR dataprocessing at the edge (e.g., using an edge compute unit) can ensurefaster responses and decision-making based on the thermal informationcaptured by the FLIR cameras. The reduction in latency and associatedbenefits may be particularly important in applications where timelyidentification is crucial, such as physical access control, securitysurveillance, safety, and/or border control, etc.

Example 4: 3D-Mapping and Localization

3D-mapping (also referred to as 3D-reconstruction or 3D-modeling), is aprocess of creating a three-dimensional representation of a real-worldobject, scene, or environment. 3D-mapping can be performed based oncapturing and processing data from multiple sources (e.g., such as depthsensors, cameras (e.g., structure-from-motion or stereo, etc.), lidar,structured-light scanners, and/or point clouds, etc.) to generate adetailed and accurate textured model of the physical world in threedimensions. The resulting 3D map or model can be used for variousapplications, including autonomous navigation, virtual reality (VR),augmented reality (AR), mixed reality (MR), gaming, architecturalvisualization, simulation, cultural heritage preservation, and more.

Autonomous navigation (e.g., implemented and/or performed by anautonomous vehicle (AV)) often uses a technique called SimultaneousLocalization and Mapping (SLAM) that enables the autonomous vehicle orrobot to build a map of an unknown environment while simultaneouslydetermining its own position within that environment. SLAM involves theestimation of both the vehicle's pose (e.g., position and orientation)and the map of the environment as the vehicle moves through thesurrounding environment and gathers corresponding sensor data of one ormore types. In some aspects, SLAM can be augmented with partial 3Dmodels, fiducials/markers, GPS, and/or Inertial Navigation System (INS)data. The main goal of SLAM is to enable autonomous systems to navigateand operate in dynamic or cluttered environments without relying ondetailed 3D models.

SLAM algorithms typically consume large amounts of sensor data that needto be processed and transmitted. By performing SLAM computations on oneor more edge compute units managed by the presently disclosed systemsand techniques for fleet and asset management for AI/ML edge computing,only relevant information—such as pose estimates of multiple autonomousvehicles (AVs) or textured-mesh 3D models—needs to be transmitted toother devices or central servers. The edge computing implementation forSLAM (among other 3D-mapping and localization techniques, algorithms,models, etc.) can reduce the amount of data transmitted over thenetwork, conserves bandwidth, and lowers the communication costsassociated with SLAM application. By eliminating the need to transmitraw sensor data, which may contain sensitive information about theenvironment, the SLAM processing is kept localized to the edge, therebyenhancing privacy and security, while also reducing the risk ofunauthorized access or data breaches.

For instance, consider an example scenario in which an autonomoustractor (e.g., a type of AV and/or a type of robotic device) maps itssurrounding environment while localizing itself within the same3D-mapped environment. In other words, the autonomous tractor cansimultaneously generate a 3D-mapping of its surrounding environmentwhile also localizing itself within the currently generated 3D-mappingof the surrounding environment. In some aspects, multiple tractorsand/or AVs can safely operate and collaborate in the same environmentwhile a respective relative pose estimate/information is estimated andtracked by a local edge compute unit of the presently disclosedmanagement and monitoring platform for AI/ML edge computation.

In such examples, performing SLAM computations on the local edge computeunits can significantly reduce the latency between sensor dataacquisition, processing, and output generation. This low latency enablesfaster updates of the localization and mapping information, therebyimproving the overall performance of SLAM. Moreover, AVs can operateautonomously even in environments with limited or intermittent networkconnectivity. As such, the local edge compute units of the presentlydisclosed management and monitoring platform for AI/ML edge computationcan continue to perform SLAM computations and update the localizationand mapping information locally, ensuring uninterrupted operation andproviding reliable localization and mapping even when offline.

In scenarios where the AVs and edge compute units are networked andconnected to cloud infrastructure, some (or all) of the edge computeunits can be configured to collaborate with the cloud for enhanced SLAMcapabilities. For instance, some (or all) of the local edge computeunits can perform local SLAM processing to provide real-time updates tothe AVs/SLAM process, while the cloud can perform additionalcomputations that are less time-sensitive or in need of real-timeimplementation (e.g., such as wide-area mapping, global optimization,and/or semantic understanding of the environment, etc.). Thiscollaborative approach balances real-time requirements with morecomputationally intensive tasks, leveraging the strengths of both edgeand cloud processing. As will be described in greater depth below, thiscollaborative cloud-edge computational approach can be implemented,mediated, managed, and/or monitored using the presently disclosedsystems and techniques for an AI/ML edge computation platform.

Example 5: Natural Language Analysis and Synthesis

Natural language analysis and synthesis have recently benefited from theuse of transformer-based machine learning models (also referred tosimply as “transformers”), based largely on the ability of transformersto adapt and extend across multiple tasks—such as (in the context ofnatural language) interpreting commands, text summarization, responsegeneration, and/or intent analysis, etc. Transformer-based models tendto be large, complex, and computationally intensive to implement due totheir deep architecture and a high number of parameters. This can posechallenges for small edge devices with limited computational resourcesand storage capacity.

In one illustrative example, the edge compute units associated with thepresently disclosed AI/ML edge computation platform can be configured tooffer sufficient memory, compute, and storage for deploying one (ormultiple) transformers at the edge. Moreover, the GPU compute capacityprovided on the edge compute units can be used to make modelsize-reduction techniques (e.g., such as model compression,quantization, and/or knowledge distillation, etc.) feasible forimplementation and use in an edge deployment. Efficient inference iscrucial for real-time or near-real-time responses. Techniques like modelpruning, quantization, or specialized hardware accelerators canadditionally be employed on the edge compute units described herein inorder to speed up inference without sacrificing performance.

In some aspects, transformer-based models, being self-contained, canoperate offline without requiring constant network access—thereby makingmany transformer-based models suitable for use or implementation inindustrial environments where continuous connectivity cannot beguaranteed. Pretrained models (e.g., pretrained transformer models) canbe fine-tuned on edge-specific data, allowing for domain adaptation andimproved performance on specific tasks. In this example, theedge-specific data can be domain and/or application-specific data (e.g.,data that is collected by the sensors associated with the same edgecompute unit, in the same environment and configured for the same task,as will be associated with the inference performed by the pretrainedtransformer model running on the edge compute unit).

In some aspects, this approach can be seen to reduce the training timeand resource requirements for natural language applications. Oncetrained and finetuned, transformer-based models may need periodicupdates or adaptations to maintain their performance over time. Thepresently disclosed edge compute units (and AI/ML edge computationmanagement platform thereof) can facilitate localized model updates orfine-tuning using edge-specific data. This allows the models to adapt tochanging conditions or requirements without relying heavily on externalservers or cloud infrastructure. The edge compute units and presentlydisclosed AI/ML edge computation platform can additionally enablecontinuous training, online and incremental learning of these modelsdeployed at the edge, thereby keeping them up-to-date on the most recentand relevant sensor data collected at the edge or elsewhere.

Another unique capability enabled by the systems and techniquesdescribed herein is collaborative or distributed inference for languagemodels (e.g., AI or ML language models). In some aspects,transformer-based models can be partitioned across varioushigh-performance compute nodes or servers (e.g., some or all of whichmay be implemented as edge compute units, at same ordifferent/distributed geographic sites or locations). The distributedhigh-performance compute nodes or servers can be configured tocollectively perform inference by sharing intermediate results or modelparameters amongst one another. This approach reduces an individualserver's computation and memory requirements while maintaining thebenefits of transformer-based models and increasing resilience.

In some examples, transformer-based models trained on large-scaledatasets may often raise privacy concerns when trained and deployed inthe cloud. Self-contained edge compute units offer the ability to storeand process sensitive data, personally identifiable information (PII),and/or protected health information (PHI) of customers on premises(e.g., at the edge/on an edge compute unit), thus minimizing the need totransmit such data to external servers. Such an approach enhancesprivacy and security by keeping the data—such as text, images, videos,electronic health records, clinical notes, financial records,confidential information—within the local network of the edge computeunit(s). Government agencies, healthcare and financial institutions canuse their own edge compute unit infrastructure to train and finetunetheir transformer-based Large Language Models (LLMs) and/or otherFoundation Models (FMs). Subsequently, the trained and finetuned LLMsand FMs can be hosted on premises (e.g., using one or more edge computeunits managed and monitored by the presently disclosed ML/AI edgecomputation management platform) behind a firewall to safeguard theknowledge encoded within the models' parameters.

Connected Edge and Cloud Implementations for AI and ML Workloads

AI and/or ML-based applications pose a set of challenges that can beuniquely addressed by edge computing. For instance, many AI and MLapplications (e.g., AI and ML models) are data intensive and may need tobe continually retrained to account for data drift. For instance, suchAI/ML applications can require (or otherwise benefit from) monitoring ofmodel degradation, regular training with new data and model parameters,etc. Consider one illustrative example in which an energy company thatoperates hundreds of oil drilling rigs around the globe generatesterabytes of data from sensors and cameras provided on each rig. Thesestreams of data can be aggregated to train models for purposes such asdetecting process anomalies, increasing safety and reliability ofoperations, automated decision making, improving system performance andthroughput, and/or updating maintenance schedules, etc.

As will be described in greater depth below with respect to FIG. 4 , oneillustrative example of a design pattern for deploying and maintainingthese AI/ML models using the presently disclosed edge compute units andAI/ML edge computation management platform can be based on using theedge compute units to perform local data capture, ingestion, andprocessing using trained ML/AI models, while using an on-cloudimplementation to perform training (including re-training and/orfinetuning) of the ML/AI models that will be implemented locally by theedge compute units.

FIG. 4 is a diagram illustrating an example of an edge computing system400 for machine learning (ML) and/or artificial intelligence (AI)workloads, where the edge computing system includes one or more localsites each having one or more edge compute units, in accordance withsome examples.

For example, a local site 402 can be one of a plurality of local sitesassociated with the edge computing system 400 and/or the presentlydisclosed AI/ML edge computation platform. For example, the plurality oflocal sites can include the local site 402 and some quantity N ofadditional local sites 402-N, each of which may be the same as orsimilar to the local site 402 described below with respect to FIG. 4 .The local site 402 can be a geographic location associated with anenterprise or other use of the presently disclosed AI/ML edgecomputation platform. The local site 402 can also be an edge location interms of data network connectivity (i.e., local site 402 is both a localgeographic location of an enterprise user and is an edge location in thecorresponding data network topography).

In the example of FIG. 4 , the local site 402 includes one or more edgecompute units 430. Each edge compute unit 430 can be configured as acontainerized edge compute unit or data center for implementing sensordata generation or ingestion and inference for one or more trained ML/AImodels provided on the edge compute unit 430. For instance, edge computeunit 430 can include computational hardware components configured toperform inference for one or more trained AI/ML models. As illustrated,a first portion of the edge compute unit 430 hardware resources can beassociated with or used to implement inference for a first AI/ML model435-1, . . . , and an N-th AI/ML model 435-N. In other words, the edgecompute unit 430 can be configured with compute hardware and computecapacity for implementing inference using a plurality of different AI/MLmodels. Inference for the plurality of AI/ML models can be performedsimultaneously or in parallel for multiple ones of the N AI/ML models435-1, . . . 435-N. In some aspects, inference can be performed for afirst subset of the N AI/ML models for a first portion of time, can beperformed for a second subset of the N AI/ML models for a second portionof time, etc. The first and second subsets of the AI/ML models can bedisjoint or overlapping.

In some embodiments, edge compute unit 430 can include computationalhardware components that can be configured to perform training,retraining, finetuning, etc., for one or more trained AI/ML models. Insome aspects, at least a portion of the computational hardwarecomponents of edge compute unit 430 used to implement the AI/ML modelinference 435-1, . . . , 435-N can also be utilized to perform AI/MLmodel retraining 433-1, . . . , 433-K and/or to perform AI/ML modelfinetuning 434-1, . . . , 434-M. For example, computational hardwarecomponents (e.g., CPUs, GPUs, NPUs, hardware accelerators, etc.)included in the edge compute unit 430 may be configured to performvarious combinations of model inference, model retraining, and/or modelfinetuning at the edge (e.g., at the local edge site 402). At least aportion of the K AI/ML models 433-1, . . . , 433-K associated with modelretraining at the edge can be included in the N AI/ML models associatedwith model inference at the edge. Similarly, at least a portion of the MAI/ML models 434-1, . . . , 434-M associated with model finetuning atthe edge can be included in the N AI/ML models associated with modelinference at the edge.

In some embodiments, for a given pre-trained AI/ML model received at theedge compute unit 430 (e.g., received from the AI/ML training clusters470 in the cloud), the edge compute unit 430 can be configured toperform one or more (or all) of model inference 435, model retraining433, and/or model finetuning 434 at the edge.

As illustrated in FIG. 4 , retraining for a plurality of AI/ML modelscan be performed simultaneously or in parallel for multiple ones of theK AI/ML models 433-1, . . . , 435-K (which as noted above can be thesame as or similar to the N AI/ML models 435-1, . . . , 435-N, or may bedifferent; and/or can be the same as or similar to the M AI/ML models434-1, . . . , 434-M, or may be different). In some aspects, retrainingcan be performed for a first subset of the K AI/ML models for a firstportion of time, can be performed for a second subset of the K AI/MLmodels for a second portion of time, etc. The first and second subsetsof the K AI/ML models can be disjoint or overlapping. Additionally, oralternatively, finetuning for a plurality of AI/ML models can beperformed simultaneously or in parallel for multiple ones of the M AI/MLmodels 434-1, . . . , 434-M (which can be the same as, similar to, ordisjoint from the N AI/ML models 435 and/or the K AI/ML models 433). Insome aspects, finetuning can be performed for a first subset of the MAI/ML models for a first portion of time, can be performed for a secondsubset of the M AI/ML models for a second portion of time, etc. Thefirst and second subsets of the M AI/ML models can be disjoint oroverlapping.

Each edge compute unit 430 of the one or more edge compute unitsprovided at each local site 402 of the plurality of local sites 402-Ncan additionally include cloud services 432, a high-performance compute(HPC) engine 434, and a local database 436. In some aspects, HPC engine434 can be used to implement and/or manage inference associated withrespective ones of the trained AI/ML models 435-1, . . . , 435-Nprovided on the edge compute unit 430.

In one illustrative example, the edge compute unit 430 can receive thetrained AI/ML models 435-1, . . . , 435-N from a centralized AI/MLtraining clusters engine 470. The AI/ML training clusters engine 470 canbe used to perform training (e.g., pre-training) of AI/ML models thatcan later be deployed to the edge compute unit 430 for inference and/orother implementations at the edge. For instance, the AI/ML trainingclusters 470 can be implemented in the cloud, as a central data centeror on-premises infrastructure for the local site(s) 420, etc. Datanetwork connectivity between edge compute unit 430 and AI/ML trainingclusters 470 can be provided using one or more internet backhaulcommunication links 440. For instance, the internet backhaul 440 can beimplemented as a fiber communication link (e.g., wired fiber opticconnectivity from the local site 402/edge compute unit 430 to internetinfrastructure that is connectable to the AI/ML training clusters 470; adirect or point-to-point wired fiber optic connectivity from the localsite 402/edge compute unit 430 to the AI/ML training clusters 470;etc.).

The internet backhaul 440 may additionally, or alternatively, beimplemented using one or more satellite communication links. Forinstance, internet backhaul 440 can be a wireless communication linkbetween edge compute unit 430/local site 402 and a satellite of asatellite internet constellation (e.g., such as the satellite internetconstellation depicted in FIG. 2 and/or FIG. 3 , etc.). In someexamples, internet backhaul link 440 can be the same as or similar toone or more of the satellite internet constellation links 214, 212, 216,of FIG. 2 (in which example the UE 230 of FIG. 2 can be the same as orsimilar to the edge compute unit 430 of FIG. 4 and network 210 of FIG. 2can be the internet providing a connection to AI/ML training clusters470 of FIG. 4 ).

In another illustrative example, the internet backhaul link 440 may bethe same as or similar to one or more of the satellite internetconstellation links 352, 362 a, 362 b, 364, 354, 374, 372, etc. of FIG.3 and the satellite internet constellation 300. In such examples, theedge compute unit 430/local site 402 can be represented as being thesame as or similar to the UE 330 of FIG. 3 ; the AI/ML training clusters470 of FIG. 4 can be the same as or similar to the data center 340 ofFIG. 3 , connected to the UE 330 (e.g., edge compute unit 430) via thesatellite internet constellation links (e.g., internet backhaul link440). Continuing in the example above, where edge compute unit 430 ofFIG. 4 is the same as or similar to UE 330 of FIG. 3 , in some aspects,it is contemplated that the edge compute unit 430 can include (orotherwise be associated with) one or more satellite transceivers forimplementing satellite connectivity to and/or from the edge compute unit430. For instance, the edge compute unit 430 can be the same as orsimilar to UE 330 of FIG. 3 , and may include, be communicativelycoupled with, and/or otherwise associated with one or more of thesatellite transceivers 312, 314, 316 of FIG. 3 . In some aspects, theone or more satellite transceivers can be integrated in or coupled to ahousing (e.g., container, where edge compute unit 430 is a containerizeddata center) of the edge compute unit 430 and used to provide satelliteconnectivity capable of implementing the internet backhaul link 440 ofFIG. 4 . In another example, the one or more satellite transceivers canadditionally, or alternatively, be provided at the local site 402 whereedge compute unit 430 is deployed.

In some aspects, the internet backhaul link 440 between edge computeunit 430 and AI/ML training clusters 470 can be used to provide uplink(e.g., from edge compute unit 430 to AI/ML training clusters 470) ofscheduled batch uploads of information corresponding to one or more ofthe AI/ML models 435-1, . . . , 435-N implemented by the edge computeunit 430, corresponding to one or more features (intermediate or output)generated by the AI/ML models implemented by edge compute unit 430,and/or corresponding to one or more sensor data streams generated byedge assets 410 provided at local site 402 and associated with the edgecompute unit 430, etc. The internet backhaul link 440 may additionallybe used to provide downlink (e.g., from AI/ML training clusters 470 toedge compute unit 430) of updated, re-trained, fine-tuned, etc. AI/MLmodels. For instance, as will be described in greater depth below, theupdated, re-trained, or fine-tuned AI/ML models transmitted overinternet backhaul link 440 from AI/ML training clusters 470 to edgecompute unit 430 can be updated, re-trained, or fine-tuned based on thescheduled batch upload data transmitted on the uplink from edge computeunit 430 to AI/ML training clusters 470. In some aspects, the updatedAI/ML models transmitted from AI/ML training clusters 470 to edgecompute unit 430 can be updated versions of the same AI/ML models 435-1,. . . , 435-N already implemented on the edge compute unit 430 (e.g.,already stored in local database 436 for implementation on edge computeunit 430). In other examples, the updated AI/ML models transmitted fromAI/ML training clusters 470 to edge compute unit 430 can include one ormore new AI/ML models that are not currently (and/or were notpreviously) included in the set of AI/ML models 435-1, . . . , 435-Nthat are either implemented on edge compute unit 430 or stored in localdatabase 436 for potential implementation on edge compute unit 430.

In some cases, the AI/ML distributed computation platform 400 can usethe one or more edge compute units 430 provided at each local site 402to perform local data capture and transmission. In particular, thelocally captured data can be obtained from one or more local sensorsand/or other edge assets 410 provided at the local site 402. Forinstance, in the example of FIG. 4 , the local edge assets/sensors 402can include, but are not limited to, one or more autonomous robots 416,one or more local site cameras 414, one or more environmental sensors412, etc. The local sensors and edge assets 410 can communicate with theedge compute unit 430 via a local network 420 implemented at or forlocal site 402.

For instance, local network 420 can be used to provide one or morecommunication links between the edge compute unit 430 and respectiveones of the edge assets 410. In one illustrative example, local network420 can be implemented as a private LTE, 4G, 5G or other privatecellular network; can be implemented as a public LTE, 4G, 5G or otherpublic cellular network; can be implemented as a WiFi, Bluetooth,Zigbee; Z-wave; Long Range (LoRa), Sigfox, Narrowband-IoT (NB-IoT), LTEfor Machines (LTE-M), IPv6 Thread, or other short-range wirelessnetwork; can be implemented as a local wired or fiber-optic network;etc. As illustrated in the example of FIG. 4 , the edge compute unit 430can receive different types of data from different ones of the edgeassets/sensors 410 and can transmit different types ofconfigurations/controls to different ones of the edge assets/sensors410. For instance, the edge compute unit 430 can receive onboard camerafeed and other sensor information (including SLAM sensor information)from the autonomous robots 416, and can transmit in response routinginstructions to the autonomous robots 416. The routing instructions canbe generated or otherwise determined based on processing the onboardcamera feed data from the autonomous robots 416 using an appropriate one(or more) of the trained AI/ML models 435-1, . . . , 435-N implementedon the edge compute unit 430 and/or using the HPC engine 434 of the edgecompute unit 430.

In another example, the edge compute unit 430 can receive local camerafeed(s) information from the local site cameras 414 and can transmit inresponse camera configuration and/or control information to the localsite cameras 414. In some cases, the edge compute unit 430 may receivethe local camera feed(s) information from the local site cameras 414 andtransmit nothing in response. For instance, the camera configurationand/or control information can be used to re-position or re-configureone or more image capture parameters of the local site cameras 414—if nore-positioning or image capture parameter reconfiguration is needed, theedge compute unit 430 may not transmit any camera configuration/controlinformation in response. In some aspects, the camera configurationand/or control information can be generated or otherwise determinedbased on processing the local camera feed data from the local sitecameras 414 using an appropriate one (or more) of the trained AI/MLmodels 435-1, . . . , 435-N implemented on the edge compute unit 430and/or using the HPC engine 434 of the edge compute unit 430.

In another example, the edge compute unit 430 can receive environmentalsensor data stream(s) information from the environmental sensors 412 andcan transmit in response sensor configuration/control information to theenvironmental sensors 412. In some cases, the edge compute unit 430 mayreceive the sensor data streams information from the environmentalsensors 412 and transmit nothing in response. For instance, the sensorconfiguration and/or control information can be used to adjust orre-configure one or more sensor data ingestion parameters of theenvironmental sensors 412—if no adjustment or re-configuration of theenvironmental sensors 412 is needed, the edge compute unit 430 may nottransmit any sensor configuration/control information in response. Insome aspects, the sensor configuration and/or control information can begenerated or otherwise determined based on processing the localenvironmental sensor data streams from the environmental sensors 412using an appropriate one (or more) of the trained AI/ML models 435-1, .. . , 435-N implemented on the edge compute unit 430 and/or using theHPC engine 434 of the edge compute unit 430.

In some examples, the systems and techniques described herein can beused to drive local storage, inference, prediction, and/or response,performed by an edge compute unit (e.g., edge compute unit 430) withminimal or no reliance on cloud communications or cloud offloading ofthe computational workload (e.g., to cloud or on-premises AI/ML trainingclusters 470). The edge compute unit 430 can additionally be used tolocally perform tasks such as background/batch data cleaning, ETL,feature extraction, etc. The local edge compute unit 430 may performinference and generate prediction or inference results locally, forinstance using one or more of the trained (e.g., pre-trained) AI/MLmodels 435-1, . . . , 435-N received by edge compute unit 430 from AI/MLtraining clusters 470. The local edge compute unit 430 may performfurther finetuning or instruction tuning of the pre-trained model to aspecified task (e.g., corresponding to at least one of model finetuning433-1, . . . , 433-M, as described previously above).

The prediction or inference results (and/or intermediate features,associated data, etc.) can be compressed and periodically uploaded byedge compute unit 430 to the cloud or other centralized location (e.g.,an on-premises location or data center, such as AI/ML training clusters470 etc.). In one illustrative example, the compressed prediction orinference results can be uploaded to the cloud via a satellitecommunication link, such as a communication link to a satellite internetconstellation configured to provide wireless satellite connectivitybetween the edge compute unit and existing terrestrial internetinfrastructure. For instance, the compressed prediction or inferenceresults can be included in the scheduled batch uploads transmitted overinternet backhaul link 440 from edge compute unit 430 to AI/ML trainingclusters 470. In some cases, the prediction or inference results can beutilized immediately at the edge compute unit 430, and may later betransmitted (in compressed form) to the cloud or centralized location(e.g., AI/ML training clusters 470). In some aspects, satelliteconnectivity can be used to provide periodic transmission or upload ofcompressed prediction or inference results, such as periodictransmission during high-bandwidth or low-cost availability hours of thesatellite internet constellation. In some cases, some (or all) of thecompressed prediction or inference results can be transmitted and/orre-transmitted using wired or wireless backhaul means where available,including fiber-optic connectivity for internet backhaul, etc.

Notably, the systems and techniques can implement the tasks andoperations described above locally onboard one or more edge computeunits 430, while offloading more computationally intensive and/or lesstime-sensitive tasks from the edge compute unit to the cloud AI/MLtraining clusters 470. For instance, the AI/ML training clusters 470 canbe used to provide on-demand AI/ML model training and fine tuning,corresponding to the updated AI/ML models shown in FIG. 4 as beingtransmitted from AI/ML training clusters 470 to edge compute unit 430via internet backhaul 440. In some aspects, the AI/ML training clusters470 can implement thousands of GPUs or other high-performance computehardware, capable of training or fine-tuning an AI/ML model usingthousands of GPUs for extended periods of time (e.g., days, weeks, orlonger, etc.). In some aspects, AI/ML training clusters 470 canadditionally, or alternatively, be used to perform on-cloud modelcompression and optimization prior to transmitting data indicative ofthe trained AI/ML models 435-1, . . . , 435-N to the edge compute unit430 for local implementation using the sensor data generated by theassociated edge assets 410. In some embodiments, the edge compute unit430 can be configured to perform a scheduled or periodic download offresh (e.g., updated or new) AI/ML models from AI/ML training clusters470 via the internet backhaul link 440 (e.g., the updated or new AI/MLmodels can be distributed from AI/ML training clusters 470 to edgecompute unit 430 in a pull fashion). In other examples, the updated ornew AI/ML models can be distributed from AI/ML training clusters 470 toedge compute unit 430 in a push fashion, wherein the AI/ML trainingclusters 470 transmit the updated or new models to the edge compute unit430 via internet backhaul link 440 as soon as the updated or new AI/MLmodel becomes available at the AI/ML training clusters 470.

Training the AI/ML models 435-1, . . . , 435-N may require massiveamounts of data and processing power, which can be more efficientlyimplemented at the AI/ML training clusters 470 (and shared across theplurality of local site 402-N edge compute units 430) rather thanimplementing individually at each of the local sites 402-N andcorresponding edge compute unit(s) 430. In some aspects, the quality ofan AI/ML model can be directly correlated with the size of the trainingand testing (e.g., validation) data used to perform the training.Furthermore, in many cases, training large AI/ML models requires runningthousands of GPUs, ingesting hundreds of terabytes of data, andperforming these processes over the course of several weeks.Accordingly, in many cases, large-scale ML/AI model training is suitedbest for cloud or on-premises infrastructure and sophisticated MLOps.For instance, the training dataset associated with training alarge-scale AI/ML model can be on the order of hundreds of TB—tens ofpetabytes (PB), or even larger. Thousands of GPUs and hours to weeks oftraining time can be needed, with the resulting size of theuncompressed, trained model exceeding hundreds or thousands of GB.

ML or AI inference (e.g., inference using a trained ML or AI model), onthe other hand, can be implemented using far fewer resources thantraining, and may performed efficiently at the edge (e.g., by edgecompute unit(s) 430 associated with the local site(s) 402 or 402-N).Indeed, in many cases, edge inferencing will provide better latency thancloud inferencing, as input sensor data generated at the edge (e.g.,using edge assets 410) does not need to transit over an internetbackhaul link 440 to the cloud region (e.g., cloud region associatedwith AIU/ML training clusters 470) before inference can begin.Accordingly, it is contemplated herein that the trained AI/ML models435-1, . . . , 435-N can be created and trained in the cloud (e.g., atAI/ML training clusters 470), and additionally can be optimized andcompressed significantly, enabling the systems and techniques describedherein to distribute the optimized, compressed, and trained AI/ML models435-1, . . . , 435-N to the edge locations associated with local sites402 and corresponding edge compute unit(s) 430 where the optimized,compressed, and trained AI/ML models will be implemented for inferencingat the edge using local sensor data from edge assets 410.

For instance, the edge compute unit 430 can use one or more of thetrained AI/ML models 435-1, . . . , 435-N to perform edge inferencingbased on input data comprising the locally/edge-generated sensor datastreams obtained from the edge assets 410 provided at the same localsite 402 as the edge compute unit 430. In some aspects, the input dataset for edge inferencing performed by edge compute unit 430 can comprisethe real-time data feed from edge assets/sensors 410, which can bebetween tens of Mbps to 10s of Gbps (or greater). The edge compute unit430 can, in at least some embodiments, include 10s of GPUs forperforming local inferencing using the trained AI/ML models 435-1, . . ., 435-N. By performing local inferencing at edge compute unit 430, aninference response time or latency on the order of milliseconds (ms) canbe achieved, significantly outperforming the inference response time orlatency achievable using cloud-based or on-premises remote inferencingsolutions.

In some aspects, the systems and techniques can be configured toimplement a continuous feedback loop between edge compute unit(s) 430and the AI/ML training cluster(s) 470. For instance, the continuousfeedback loop can be implemented based on using the edge compute unit(s)and associated edge assets/sensors 410 to capture data locally, performinference locally, and respond (e.g., based on the inference) locally.The edge compute unit(s) 430 can be additionally used to compress andtransmit features generated during inference from the source data and/orto compress and transmit inference results efficiently to the AI/MLtraining clusters 470 (among other cloud or on-premises locations). Inthe continuous feedback loop, training and fine-tuning can subsequentlybe performed in the cloud, for instance by AI/ML training clusters 470and using the batch uploaded sensor data and/or features uploaded by theedge compute unit(s) 430 to AI/ML training clusters 470. Based on thetraining and fine-tuning performed in the cloud by the AI/ML trainingclusters 470, new or updated AI/ML models are distributed from the AI/MLtraining clusters 470 back to the edge (e.g., to the edge computeunit(s) 430 and local site(s) 402). This continuous feedback loop fortraining and fine-tuning of AI/ML models can be seen to optimize theusage of cloud, edge, and bandwidth resources. The same AI/ML model maybe finetuned across multiple edge nodes to optimize the usage ofavailable compute at the nodes and the cloud. For instance, an AI/MLmodel can be finetuned across a set of edge nodes comprising at leastthe edge compute unit 430 and one or more edge compute units included inthe additional local sites 402-N. In some cases, the distributedfinetuning of an AI/ML model across multiple edge nodes can be mediated,supervised, and/or controlled, etc., by the AI/ML training clustersengine 470 (e.g., or various other cloud entities). In some examples,the distributed finetuning of an AI/ML model across multiple edge nodescan be supervised and/or controlled, etc., by a selected one or moreedge nodes of the set of edge nodes associated with the distributedfinetuning of the model. In one illustrative example, distributedfinetuning or retraining of an AI/ML model across multiple edge nodescan be orchestrated by a respective fleet management client 770 of FIG.7 that is implemented at or by each of the multiple edge nodes.

Edge AI/ML Monitoring and Management Platform—Software Stack andServices

FIG. 5 is a diagram illustrating an example software stack 500associated with implementing an edge computing system for ML and/or AIworkloads, in accordance with some examples. In particular, FIG. 5depicts an example platform software stack 502 that can be used toprovide single pane management of a fleet of deployed edge computeunits, connected sensors and assets associated with an edge computeunit, and/or one or more AI/ML models that are pre-trained and deployedon an edge compute unit to process or otherwise analyze raw sensor datagenerated by the connected sensors and assets associated with the edgecompute unit.

As illustrated, the example platform software stack 502 can includedomain-specific application services 560, such as the example computervision services 562 and industrial internet of things (IIoT) services564 that are depicted as specific examples of domain-specificapplication services. The example platform software stack 502 canadditionally include a qualified application repository 550, which canbe implemented as a repository of pre-trained and/or pre-configured AIand/or ML applications capable of running on the edge compute unit toperform specific tasks or computations using specific types of sensorsand/or sensor data streams available to or otherwise associated with theedge computing device. In some aspects, the qualified applicationrepository 550 can be implemented as an application marketplace forthird-party AI and/or ML applications that can be deployed to the edgecompute unit for providing particular or desired computationalcapabilities and workflows. In comparison to the domain-specificapplication services 560, it is contemplated that in at least someembodiments, the domain-specific application services 560 can beprovided as first-party or platform-level AI and/or ML applications andassociated services, while the qualified application repository 550 canbe used to provide third-party or developer-level AI and/or MLapplications and associated services for implementation on the edgecompute unit.

In some aspects, the platform software stack 502 can further includenative or platform applications 540. In some embodiments, theapplication repository 550 can be a cloud-based repository of qualifiedAI/ML applications for deployment on one or more edge compute units 430.For instance, the application repository 550 can be a cloud-basedmarketplace for the management of customer and platform ML/AIapplications. In some cases, customer applications can bethird-party/developer applications, and the platform applications may bethe same as or similar to the native/platform applications 540 and/orthe domain-specific application services 560.

The native/platform applications 540 can be differentiated from thedomain-specific application services 560 on the basis that thenative/platform applications 540 are provided in a manner the same as orsimilar to the third-party or developer level AI/ML applications 550, inthat both the native/platform applications 540 and third-party AI/MLapplications 550 can be configured to perform a specific sensor dataprocessing or analysis task that may make use of or call one or more ofthe domain-specific application services 560. In other words, thedomain-specific application services 560 can be implemented as modules,engines, APIs, etc., that are configured to perform specific tasks in ageneric manner that is independent of the specific implementation orintended use case of one of the native/platform applications 540 orthird-party/developer applications 560.

For instance, FIG. 5 depicts the example domain-specific applicationservices 560 in the form of computer vision services 562 and IIoTservices 564. Various additional domain-specific application services560 can be implemented or provided without departing from the scope ofthe present disclosure. For instance, the domain-specific applicationservices 560 can include one or more of natural language services 563,reinforcement learning services 566, augmented and mixed realityservices 565, robotic platform services 567, and/or localization,mapping, and navigation services 568, etc., among various otherservices. In one illustrative example, a domain-specific applicationservice 560 (such as computer vision services 562) can be utilized byone or more native/platform applications 540 and can be utilized by oneor more third-party/developer applications 550. For instance, thenative/platform applications 540 can include a native/platform objectrecognition application that can be configured to perform objectrecognition of a specified object type or class within a specified orselected type of input data (e.g., human recognition in FLIR cameradata). To implement the human recognition in FLIR data native/platformapplication 540, a corresponding native/platform application 540 can beprovided that makes use of the computer vision services 562 to providethe recognition functionality. In other words, the native/platformapplication 540 can be built on top of or incorporating the computervision services 562, with the native/platform application 540 performingtasks such as data ingestion, organization, pre-processing, etc., thatare needed to convert the raw FLIR camera data into the expected orrequired input format for processing the FLIR camera data using thecomputer vision services 562.

A similar structure can be utilized for implementing thethird-party/developer applications 550 to make use of the variousdomain-specific application services 560. In some aspects, a same orsimilar functionality can be provided by the third-party/developerapplications 550 and the native/platform applications 540. In otherexamples, one or more functionalities and/or domain-specific applicationservices 560 may be configured for use exclusively by one or more of thenative/platform applications 540 (e.g., without the possibility ofoverlapping, same, or similar functionality by one of thethird-party/developer applications 550). In some cases, thenative/platform applications 540 can be implemented as Docker orKubernetes Container environments that are deployable on or to the edgecompute units described herein. In some aspects, native/platformapplications 540 may be made available and/or distributed using the samemarketplace mechanism associated with distributing thethird-party/developer applications (e.g., the qualified applicationrepository 550 may, in some embodiments, include both first-partyplatform/native applications 540 and third-party/developerapplications). In other examples, native/platform applications 540 maybe pre-loaded or pre-configured on the edge compute unit(s) at the timeof deployment, with only the third-party/developer applications 550being configurable or loadable to the edge compute unit at a later time(e.g., via selection in the qualified application repository 550).

In some embodiments, the platform software stack 502 can additionallyinclude one or more knowledge bases and/or local data storages 545,which may be associated with and utilized by one or more of thethird-party AI/ML applications 550 and/or one or more of the nativeplatform applications 540. For instance, some applications may requireknowledge bases and databases 545 to be hosted locally for use by theapplications. The knowledge bases and databases 545 can be used to storeinformation corresponding to a particular task or analytical/dataprocessing operation implemented by an application that uses theknowledge bases and databases 545. In some cases, the knowledge basesand databases 545 can be logically delineated or separated on the basisof the corresponding application(s) that make use of each given one ofthe knowledge bases and databases 545. In some cases, the knowledgebases and databases 545 can be combined for different applications. Insome embodiments, the knowledge bases and databases 545 can be includedin and/or otherwise associated with the local database 436 of FIG. 4 .In some aspects, one or more of the knowledge bases and databases 545can be implemented locally at the edge (e.g., at local edge site 402 ofFIG. 4 ), can be implemented in the cloud (e.g., a cloud associated withAI/ML training clusters 470 of FIG. 4 ), and/or can be implemented as acombination of edge and cloud resources.

The knowledge bases and databases 545 may also be referred to herein asa “local datastore/knowledge base” and/or a “local datastore andknowledge base.” In some aspects, the local datastore and knowledge basecan include content and information obtained over a data network such asthe internet. For instance, local datastore and knowledge base contentand information can be populated, updated, deliver, etc., via theinternet backhaul link 440 shown in FIG. 4 between the local edge site402 and the cloud cluster(s) 470. In some embodiments, local datastoreand knowledge base 545 can be served content over a satellite internetconstellation-based CDN (e.g., such as a satellite CDN describedpreviously above with respect to FIG. 3 ). In some embodiments, thelocal datastore and knowledge base(s) 545 can be implemented at the edgecompute unit 430 of FIG. 4 , as noted above. It is further noted thatthe local datastore and knowledge base(s) 545 can be implemented basedon or corresponding to a respective edge compute unit service (e.g., acorresponding edge service for local datastore and knowledge base(s) 545can be included in the edge compute unit services 605 of FIG. 6 ,described subsequently below).

In one illustrative example, the local datastore and knowledge base(s)545 can include publicly available data network content (e.g., webcontent). Notably, the local datastore and knowledge base(s) 545 canfurther include domain or niche knowledge of processes, devices, assets,personnel, tasks, tools, activities, etc., that are pertinent to thelocal and global operations of a user (e.g., enterprise user) of theedge compute unit and associated platform system(s) of the presentdisclosure. In some aspects, this domain or niche knowledge representedwithin the local datastore and knowledge base(s) 545 can be broadlyreferred to as domain-specific information, task-specific information,operations-specific information, private, proprietary or non-publicinformation, etc. For instance, the local datastore and knowledgebase(s) 545 can include domain or operations-specific data generated atthe edge and ingested to one or more edge compute units 430 within thefleet of edge compute units of an enterprise user. This local domain oroperation-specific edge-generated information may include, but is notlimited to, information such as maintenance records, user reports,machine reports and logs, work summaries, activity reports, device/assetmanuals, sensor specifications, etc.—some (or all) of which may beconsumed at the edge by one or more AI/ML models. For instance,information and data from local datastore and knowledge base(s) 545 canbe consumed at the edge during inference using one or more trained AI/MLmodels, may be consumed at the edge during retraining of one or morepre-trained AI/ML models, and/or may be consumed at the edge duringfinetuning of one or more pre-trained AI/ML models.

In some examples, the local datastore and knowledge base(s) 545 caninclude data that may be used for finetuning and/or instructing one ormore AI/ML models for performing a specific task at the edge. Forinstance, the local datastore and knowledge base(s) 545 can include datafor finetuning and/or instructing one or more of the AI/ML models 435-1,. . . , 435-N of edge compute unit 430 of FIG. 4 for performing aspecific inference task at the edge. Local datastore and knowledge base545 data can be utilized for one or more of the model finetuninginstances 434-1, . . . , 434-M depicted for edge compute unit 430 inFIG. 4 and/or can be utilized for one or more of the model retraininginstances 433-1, . . . , 433-K also depicted for edge compute unit 430in FIG. 4 .

In some aspects, the platform software stack 502 can further include atelemetry and monitoring engine 530 (also referred to herein as the“observer” or “observer engine”), a remote fleet management controlplane 520, and a secure edge operating system (OS) 510. In someexamples, one or more of the components of platform software stack 502can be implemented in the cloud (e.g., remote from the edge, such asremote from the local site 402 and/or edge compute unit 430 of FIG. 4 ).Components of platform software stack 502 that are implemented in thecloud may be implemented with and/or collocated with the AI/ML trainingclusters 470 of FIG. 4 , or may be separate from the AI/ML trainingclusters 470 of FIG. 4 . In some cases, one or more of the components ofplatform software stack 502 can be implemented at the edge, for instanceat local site 402 and/or on edge compute unit 430 of FIG. 4 .

In one illustrative example, the domain-specific application services560 can be implemented in the cloud, can be implemented at the edge, orcan be implemented using a combination of cloud and edge deployments.For instance, domain-specific application services 560 may be providedlocally on edge compute unit 430 of FIG. 4 , particularly for instanceswhere a given domain-specific application service 560 is used often bythe edge compute unit 430 (e.g., is called or used by an application orAI/ML model running on the edge compute unit 430 of FIG. 4 , such as athird-party/developer application from repository 550 and/or anative/platform application 540). In some examples, domain-specificapplication services 560 may be provided as cloud services that arereached from edge compute unit 430 via internet backhaul link 440. Forinstance, domain-specific application services 560 that are rarely orhave yet to be used by edge compute unit 430 can remain as cloudservices until a greater need emerges at some point in the future forthe domain-specific application service 560 to be implemented locally atedge compute unit 430.

For instance, the process of installing a new AI/ML application or modelto edge compute unit 430 (either in the form of a third-partyapplication from repository 550 or in the form of a native/platformapplication 540) can include checking the application to be installedfor dependencies on one or more domain-specific application services560. A first portion of the dependencies for the to-be-installedapplication may already reside at the edge (e.g., may already beinstalled or available at edge compute unit 430) and no further actionis needed. A second or remaining portion of the dependencies for theto-be-installed application may be new to the edge compute unit 430 ofFIG. 4 (i.e., are not yet installed or available at the edge/at edgecompute unit 430). In this case, installing a new AI/ML application toedge compute unit 430 can include obtaining and/or installing local edgecopies or implementations of one or more domain-specific applicationservices 560 that are needed or used by the to-be-installed applicationand were identified as not yet residing at the edge.

In some embodiments, the qualified application repository 550 (e.g.,implemented as a marketplace of third-party AI/ML applications for edgecompute unit 430) can reside in the cloud, with individual ones of theavailable AI/ML applications installed to edge compute units 430 basedon an enterprise user selection of the AI/ML applications from thecloud-hosted qualified application repository 550. Similarly,native/platform applications 540 may reside in the cloud prior toinstallation on the edge compute unit 430. In some embodiments, some (orall) of the native/platform applications 540 can be pre-installed orpre-configured locally on the edge compute units, and may optionally bemade also available in the cloud.

The observer engine 530 (e.g., telemetry and monitoring engine 530) canbe implemented at the edge (e.g., on edge compute units 430) and/or canbe implemented in the cloud. For instance, each edge compute unit 430can run an instance of the observer engine 530 (or a portion thereof)locally, to capture telemetry and other critical environmentalmonitoring and observation data at the edge compute unit 430 and/orlocal site 402 associated with the edge compute unit 430. The telemetryand monitoring data from the local instance of the observer engine 530at each edge compute unit 430 can be transmitted to a correspondingobserver engine instance 530 running in the cloud.

For example, the local observer engine 530 instance at edge compute unit430 can upload host and satellite constellation level metrics to aglobal observer engine instance that is associated with the cloud-basedremote fleet management control plane 520. The cloud-based remote fleetmanagement control plane 520 can be used to provide a single pane ofglass interface to the fleet of edge compute units 420 and local sites402 (e.g., 402, . . . , 402-N), and can display the observer enginetelemetry and monitoring data from various edge compute units 430 usinga global management console (also referred to herein as a globalmanagement portal). For instance, the remote fleet management controlplane 520 can include or provide one or more graphical user interfaces(GUIs) indicative of various telemetry and monitoring data obtained fromthe deployed edge compute units 430 and local sites 402 (e.g., such asthe GUIs 800 and 900 of FIGS. 8 and 9 , respectively, discussed ingreater detail below).

The secure edge OS 510 can be installed on the edge compute units 430,and may be used to provide operating system functionality forimplementing computation operations and other functionalities at theedge compute unit 430 itself. The secure edge OS 510 can additionally beused to provide an interface and communications between the edge computeunit 430 and the remaining portions of the platform software stack 502.For instance, the secure edge OS 510 can be configured to communicatewith the cloud-based components of the platform software stack 502,including the observer engine 530, remote fleet management control plane520, domain-specific application services 560, qualified applicationrepository 550, and/or native/platform applications 540.

FIG. 6 is a diagram illustrating an example architecture 600 forimplementing platform (e.g., global) services 602 and edge computeservices 605 of an edge computing system for ML and/or AI workloads, inaccordance with some examples. In some embodiments, the platformservices 602 of FIG. 6 can be the same as or similar to the platformsoftware stack 502 of FIG. 5 . In some cases, the edge compute services605 of FIG. 6 can be the same as or similar to one or more servicesimplemented on or for the edge compute unit 430 of FIG. 4 . Forinstance, then edge compute services 605 of FIG. 6 can be deployed toand/or utilized by an edge compute unit such as edge compute unit 430 ofFIG. 4 .

In some aspects, the platform services 602 can include an applicationrepository 650, which may be the same as or similar to the qualifiedapplication repository 550 of FIG. 5 ; a telemetry and monitoringobserver engine 630, which may be the same as or similar to thetelemetry and monitoring observer engine 530 of FIG. 5 ; and a globalmanagement console 620 that may be the same as or similar to the remotefleet management control plane 520 of FIG. 5 . For instance, globalmanagement console 620 can be used to display one or more of the exampleGUIs 800 and 900 depicted in FIGS. 8 and 9 , respectively. The platformservices 602 can additionally include a Software-Defined Networking(SDN) network configuration service 660, a device/asset lifecyclemanagement (DLM) engine 670, and a satellite edge connectivitymanagement engine 680, each of which are described in turn below.

With respect to the edge compute unit services 605 of FIG. 6 , asillustrated the edge compute unit services 605 can include user andplatform applications 655, SDN network provisioning and managementengine 665, a fleet management daemon 673, cloud connector services 677,a telemetry and monitoring stack 635, bare metal services 617, an edgeOS 615, and a local management console 625. In some aspects, the userand platform applications 655 can be the same as or similar to (and/orcan include) the trained AI/ML, model inference instances 435-1, . . . ,435-N depicted in and described above with respect to the edge computeunit 430 of FIG. 4 . The user and platform applications 655 of FIG. 6can additionally be associated with one or more (or all) of the AI/MLmodel retraining instances 433-1, . . . , 435-K depicted within edgecompute unit 430 of FIG. 4 . In some aspects, the user and platformapplications 655 of FIG. 6 can be additionally associated with one ormore (or all) of the AI/ML mode finetuning instances 434-1, 434-Mdepicted within edge compute unit 430 of FIG. 4 . In some cases, the SDNnetwork provisioning and management engine 665 of the edge compute unitservices 605 can correspond to the SDN network configuration service 660of the platform services 602. In some embodiments, the fleet managementdaemon 673 can be associated with the cloud-based DLM engine 670 of theplatform services 602. The edge-based telemetry and monitoring stack 635can correspond to the cloud-based telemetry and monitoring observerengine 630 of the platform services 602. In some examples, the edge OS615 can be the same as or similar to the secure edge OS 510 of FIG. 5 .

In some embodiments, the edge compute unit services 605 can include oneor more edge services associated with implementing, maintaining,updating, using, etc., local datastore and knowledge base information atand for an edge compute unit. For instance, the edge compute unitservices 605 can include one or more edge services associated withimplementing, maintaining, updating, using, etc., the local datastoreand knowledge base(s) 545 depicted in FIG. 5 and described previouslyabove. In some embodiments, one or more of the edge connector services677 can be associated with implementing the local datastore andknowledge base(s) 545 of FIG. 5 . In some aspects, one or more dedicatededge connector services (not shown) within the edge compute unitservices 605 can be associated with implementing the local datastore andknowledge base(s) 545 of FIG. 5 .

Global Management Console

In one illustrative example, the global management console 620 canprovide users with single pane of glass access, insight, and/ormanagement corresponding to each of the remaining modules of theplatform services 602 and/or of the edge compute unit services 605. Forinstance, the global management console 620 can provide one or more GUIscorresponding to each of the platform services 602. For instance, theglobal management console 620 can be a cloud-hosted global managementconsole configured to implement a comprehensive asset management portal.

In some embodiments, and as will be described in greater detail below,the global management console 620 can provide GUIs for monitoring,managing, configuring, interacting with, etc., one or more of: satelliteinternet constellation connectivity (e.g., based on information from thetelemetry and monitoring observer engine 630 and/or satellite edgeconnectivity management engine 680 included in the platform services602); host-level metrics (e.g., edge compute unit 430-level metrics)based on information from the telemetry and monitoring observer engine630 included in the platform services 602 and/or the telemetry andmonitoring stack 635 included in the edge compute unit services 605;support forms; management functions (e.g., pause/move satellite internetconstellation connectivity service, change plans), login andadministrative or user account management tasks; AI/ML marketplace anddeployment functionality associated with the cloud-based AI/MLapplication repository 650 and edge-deployed AI/ML user and platformapplications 655; user management; notifications and alarms; etc.

As contemplated herein, the global management console 620 can provide acomprehensive and unified software solution designed to simplify andstreamline the management of an enterprise customer's fleet ofedge-deployed assets, including edge compute units 430 and/or otherconnected sensors and edge assets 410 deployed at a local edge site 402in conjunction with one or more edge compute units 430. In oneillustrative example, global management console 620 can be configured toprovide a single intuitive interface with one or more GUIs correspondingto each of the platform services 602 and/or corresponding to one or moreof the edge compute unit services 605. Using the global managementconsole 620 and its corresponding GUIs, the systems and techniquesdescribed herein can be used to implement complete and superior remotevisibility and control over all aspects of edge asset and edge computedevice 430 operations.

For instance, the global management console 620 can be used to providephysical asset management with full oversight of the location, power,storage, data, and connectivity associated with a fleet of edge computedevices 430 and connected edge assets 410 of a local edge site 402. Thephysical asset management provided by global management console 620 canbe used to achieve optimal resource allocation and performance at theedge. The platform services 602 can be used to monitor real-time energyconsumption, data usage, utilized storage, and/or network connectivity(among various other parameters and data streams) to minimize downtimeand maximize efficiency at the edge.

For instance, FIG. 9 depicts an example GUI 900 that can be presented bythe global management console 620 and used to monitor edge compute unitmetrics (e.g., “Host Metrics”) such as CPU utilization (current andhistorical), memory utilization (current and historical), storageutilization (current and historical), GPU or any high-performancecompute (e.g., NPU, hardware accelerator, etc.) utilization (current andhistorical), as well as to provide visualizations of respectivemonitored edge compute unit metrics. In some cases, the globalmanagement console 620 and/or example monitoring GUI 900 can include GUIelements for setting, defining, and/or triggering alerts based onmonitored parameters exceeding one or more thresholds or otherwisesatisfying user or platform-specified rules and conditions (e.g., “SetAlert” in FIG. 9 ). In addition to the “Host Metrics” shown in theexample GUI 900 of FIG. 9 , the global management console 620 canprovide physical asset management that includes visibility and insightinto “Power and Environmental” telemetry and monitoring data, which maybe obtained and monitored based on the cloud-based telemetry andmonitoring observer 630 included in the platform services 602 and theedge-based telemetry and monitoring stack 635 included in the edgecompute unit services 605 (both of which are described in greater detailbelow).

In some aspects, the global management console 620 can provide physicalasset management that includes visibility and insight into “AppMetrics,” as depicted in the example monitoring GUI 900 of FIG. 9 . The“App Metrics” can correspond to monitoring information for AI/MKworkloads implemented at the edge, such as on an edge compute device430. For instance, the “App Metrics” may correspond to one or more (orall) of the AI/ML inference workloads 435-1, . . . , 435-N depictedrunning on the edge compute unit 430 of FIG. 4 .

In some aspects, the global management console 620 can be used toprovide application management for deployed AI/ML applications runningon the edge compute unit 430. For instance, global management console620 can provide application management for the deployed user andplatform AI/ML applications 655 included in the edge compute unitservices 605 running on edge compute unit 430. In some aspects, globalmanagement console 620 can provide application management for deployedAI/ML applications to simplify the deployment and management of theAI/ML applications with asset-aware resource provisioning. In suchexamples, enterprise users of the global management console 620 caneasily deploy, update, and remove AI/ML applications on multiple assets(e.g., multiple edge compute units 430) at once. In some embodiments,application management via global management console 620 can be combinedwith or implemented in conjunction with the cloud-based applicationrepository 650 that is used to install and manage some (or all) of theuser and platform AI/ML applications 655 on the edge compute unit 430.

In some embodiments, the global management console 620 can be used toprovide workload management for the deployed AI/ML applications runningon the edge compute unit 430. For instance, global management console620 can provide workload management for some (or all) of the deployeduser and platform AI/ML applications 655 of FIG. 6 , for some (or all)of the deployed AI/ML model inference instances 435-1, . . . , 435-Nrunning on the edge compute unit 430 of FIG. 4 , etc. In some cases,workload management can be implemented based on using the globalmanagement console 620 to manage AI/ML workloads deployed to one or moreedge assets of an enterprise user (e.g., deployed to one or more edgecompute units 430/local sites 402 of the enterprise user).

Workload management for AI/ML workloads can include, but is not limitedto, automatic resource provisioning, sensor suite selection, jobassignment, job cancellation features, etc. In some aspects, enterpriseusers of the global management console 620/platform services 602 can seewhich assets (e.g., edge compute units 430, or assets/compute componentsthereof) are currently available and capable of performing an AI/MLworkload either now or at a scheduled time in the future. In someembodiments, workload management for AI/ML workloads on an edge computedevice 430 can include scheduling the AI/ML workload for a future timewhen bandwidth, data, computation, and/or energy is projected orestimated to be more available, is projected or estimated to be cheaper,etc.

In still further example, the global management console 620 can be usedto provide security and access control to enterprise users' local sites402 and/or to the edge compute units 430 and/or connected edge assets410 deployed to the respective local sites 402. For instance, theenterprise users may utilize global management console 620 to manage thephysical, network, and software security associated with their edgeassets, including (but not limited to), actions such as user creation,access permission configuration, and credential management, etc. Theglobal management console 620 and security and access control featurescan be utilized to ensure that only authorized personnel of theenterprise user can access sensitive data and resources, whilemaintaining full audit trails at the edge compute units 430 and localsites 402 (as well as cloud user environments 690) for compliancepurposes.

Local Management Console

In some cases, the local management console 625 can be an offline oroffline-capable, local edge version or implementation of the globalmanagement console 620 of platform services 602. In some cases, thelocal management console 625 can be similar to an offline and/or localedge implementation of the remote fleet management control plane 520 ofFIG. 5 . For instance, the local management console 625 can be the sameas or similar to the global management console 520, 620, with theconstraint that the local management console 625 operates withoutreliance on connection to a remote network, data center, cloud,on-premises infrastructure, etc. For instance, in the context of theexample of FIG. 4 , the local management console 625 may operate withinlocal site 402, using the available local network(s) 420 to communicatewith and between edge compute unit 430 and the various edge assets 410,and without using communications over internet backhaul link 440 orto/from any cloud or on-premises data center locations.

For instance, the local management console 625 can be used to implementa customer local portal at the edge compute unit 430 and/or local site402 depicted in FIG. 4 . The local management console 625 can be an outof band management portal that acts as a local management portal in caseof lost connectivity to the (cloud-based) global managementconsole/portal 620. For instance, if the internet backhaul link 440 ofFIG. 4 goes down or becomes unavailable, local management can still beperformed for the edge compute unit 430 and connected local edge assets410 using the local management console 625, which does not depend oncloud connectivity or the internet backhaul link 440 to operate. In someaspects, the local management console 625 can be included or implementedin the control plane of the edge compute unit 430/edge compute unitservices 605. In some embodiments, the local management console 625 canprovide a read-only view into HCI and satellite internetconnectivity/constellation metrics that can be determined locally at theedge compute unit 430.

Application Repository/Marketplace

In some examples, global management console 620 can provide a first GUIcorresponding to application repository 650, which can be used to viewavailable AI/ML applications that can be deployed to an edge computeunit (e.g., an edge compute unit 430 of FIG. 4 and/or other edge computeunit corresponding to the edge compute unit services 605 of FIG. 6 ). Insome cases, the first GUI corresponding to application repository 650can display a listing of only third-party/developer AI/ML applications.In some examples, the application repository 650 GUI presented by globalmanagement console 620 can display a listing of only first-partynative/platform AI/ML applications, such as the native/platformapplications 540 of FIG. 5 . In some cases, the application repository650 GUI can display a listing of one or more (or all) of thedomain-specific application services 560 of FIG. 5 , although in somecases the domain-specific application services 560 may be hidden or notlisted in the application repository 650 GUI. In some aspects, theapplication repository 650 GUI can display a listing that combines oneor more of the third-party/developer AI/ML applications, first-partynative/platform AI/ML applications, and/or the domain-specificapplication services.

As illustrated in FIG. 6 , the application repository 650 of platformservices 602 can correspond to the user and platform applications 655 ofthe edge compute unit services 605. For instance, the user and platformapplications 655 can comprise a selection or a subset of the completelisting of applications available in application repository 650, wherethe selection or subset of the AI/ML applications represents those AI/MLapplications that an enterprise user has selected for installation ordeployment on the edge compute unit 430. Installing or deploying anAI/ML application on the edge compute unit 430 can be based on includingthe AI/ML application in the user and platform applications 655 of theedge compute unit services 605. Installing or deploying an AI/MLapplication on the edge compute unit 430 may additionally includeconfiguring or providing on the edge compute unit 430 (or at local edgesite 402) one or more corresponding sensors, devices, and/or roboticassets, etc., associated with, used by, or required for the particularAI/ML application.

In some aspects, the edge compute unit services 605 can be connected tovarious sensors, external devices (e.g., displays, handhelds, personaldevices, etc.), robotic assets, etc., that are provided or deployed atthe edge (e.g., deployed in association with one or more edge computeunits 430). For example, one or more edge services of the edge computeunit services 605 can be used to configure and manage connectivity tothe sensors, external devices, robotic assets, etc., at the edge. Insome examples, one or more edge services of the edge compute unitservices 605 can be used to configure and manage the local network 420connectivity shown in FIG. 4 between the edge compute unit 430 and theautonomous robotic assets 416, local site cameras 414, environmentalsensors 412, etc. More generally, in some examples, the one or more edgeservices of the edge compute unit services 605 can be used to configureand manage connectivity to the edge assets 410 across one or more localedge sites 402 (e.g., including additional local site(s) 402-N) and/oracross one or more edge compute units 430.

In some embodiments, the AI/ML applications that can be deployed on agiven edge compute unit 430 can depend at least in part on the availablecompute, storage, and local connectivity capabilities or options at theedge compute unit. For instance, AI/ML applications can be associatedwith corresponding minimum required computational hardware orcapabilities, minimum required storage capacity or availability, minimumrequired local data I/O or read/write speed, minimum required memorycapacity, minimum required local connectivity, etc. In some embodiments,the application repository 650 can be indicative of minimum requirementsor required edge configurations for implementing a particular AI/MLapplication that is made available via the application repository 650.In some aspects, the requirements or configurations for implementing aparticular AI/ML application can apply to both the available hardware ofthe edge compute unit 430 as well as the available edge assets 410 forthe edge compute unit 430. For instance, some AI/ML applicationsdeployable from the application repository 650 may require certainconfigurations or quantities of various types of sensors, externaldevices, and/or robotic assets, etc., among various other examples ofconnected or connectable edge assets 410. In some examples, an AI or MLapplication that can be deployed on an edge compute unit 430 (e.g., thatmeets or exceeds the corresponding minimum requirements or capabilitiesfor the given AI or ML application) can be referred to as an AI or MLapplication qualified for deployment on the edge compute unit.

In one illustrative example, an AI/ML SLAM (simultaneous localizationand mapping) application may be unable to be deployed to an edge computeunit (e.g., unable to be deployed into the user and platformapplications 655) unless the edge compute unit has both the requisitelocal network (e.g., WiFi, 4G, 5G, etc.) connectivity and bandwidth andthe appropriate camera hardware (e.g., at the necessary resolution,frame rate, field-of-view, lighting, etc.) connected to the edge computeunit over the local network. In another illustrative example, one ormore (or all) of the respective AI/ML applications included in theplurality of AI/ML applications of the application repository 650 caninclude corresponding requirements or configurations associated withinput data for the respective AI/ML application. For instance, thecorresponding requirement(s) or configuration(s) information fordeploying an AI/ML application from the application repository 650 to anedge compute unit 430 can be indicative of one or more types of inputdata required to run the AI/ML application. In some embodiments, theinput data requirement(s) can be indicative of a data type(s) requiredby the AI/ML application, optionally or preferably used by the AI/MLapplication, etc. The input data requirement(s) may additionally, oralternatively, be indicative of data types that are not supported orused by the AI/ML application, etc. In one illustrative example, thedifferent data type requirements or configurations for an AI/MLapplication of application repository 650 can correspond to one or moreof a structured data type(s), semi-structured data type(s), and/orunstructured data type(s), etc. In some embodiments, the different datatype requirements or configurations for an AI/ML application ofapplication repository 650 can correspond to one or more of amachine-generated data type(s), sensor-generated data type(s),user-generated data type(s), etc. In some embodiments, the input datarequirement(s) and/or configuration(s) can be included in a connectededge asset requirement of an AI/ML application deployable from theapplication repository 650, and/or can be included in an edge computedevice requirement of an AI/ML application deployable from theapplication repository 650.

In one illustrative example, the platform applications represented inthe software stack (e.g., included in the user and platform applications655 deployed at the edge, included in the application repository 650 inthe cloud, etc.) can be used to enable enterprise user's AI/ML workloadsto be run on the edge compute units 430. For instance, the platformAI/ML applications can be based on a core orchestration layer ofplatform services 602/edge compute unit services 605 to account forredundancy and resiliency. In some embodiments, the platform AI/MLapplications can utilize or be based on open-source distributedcomputing platforms for data processing, storage, and movement (e.g.,Spark, MinIO, Kafka, etc.). In some aspects, the platform AI/MLapplications can be fully managed applications, for instance in terms oftuning, updates, addressing of critical vulnerabilities, etc.

In some embodiments, the application repository 650 can includefirst-party/platform AI/ML applications and can includethird-party/developer AI/ML applications. In some examples,first-party/platform AI/ML applications can be configured as a coresuite of AI and ML applications, models, networks, etc., that aretrained and selected to solve or otherwise address various unsolvedand/or underserved enterprise user use cases in the edge computingspace. In one illustrative example, the first-party/platform AI/MLapplications can be deployed and managed through a cloud-basedapplication marketplace (e.g., application repository 650). Thefirst-party/platform AI/ML applications can be tuned and right-sized(e.g., scaled up or down, compressed, optimized, etc.) for the varioushardware configurations available for the edge compute units 430, andcan be designed or purpose-built to maximize resource utilize at theedge and when deployed on the edge compute units 430. For instance, theedge compute unit 430 can be associated with a plurality ofpre-configured compute hardware options. Some (or all) of thefirst-party/platform AI/ML applications can be provided to thecloud-based application repository in a form or version optimallycorresponding to various ones of the plurality of pre-configured computehardware options available for implementing the edge compute unit. Forinstance, a first compute hardware configuration of the edge computeunit 430 may be more powerful (e.g., more GPUs, more powerful GPUs, moreRAM, etc.) than a second compute hardware configuration of the edgecompute unit 430 (e.g., fewer GPUs, less powerful GPUs, fewer availableGPU cores, lower GPU data transfer speed, less RAM, etc.). Some (or all)of the pre-trained and pre-tuned first-party/platform AI/ML applicationscan have at least a first version optimized to run on the first computehardware configuration of the edge compute unit 430 and a second(smaller and more lightweight) version optimized to run on the secondcompute hardware configuration of the edge compute unit 430, etc.

In some cases, application repository 650 can be implemented as acloud-based marketplace for the management of customer and platformAI/ML applications (e.g., including the deployed user and platformapplications 655 provided in the edge compute unit services 605). Forinstance, the application repository 650 (e.g., AI/ML applicationmarketplace) can be used to provide fully managed applications that aresubjected to a qualification and certification process prior to beingon-boarded to the cloud-based application repository/marketplace 650 fordeployment to various enterprise user local edge sites 402 andcorresponding edge compute units 430. In some cases, the qualificationand certification process for onboarding a third-party/developer ML/AIapplication to the marketplace can be performed to determine runtimefidelity and viability of the third-party ML/AI application fordeployment on the edge compute units 430. In some embodiments, theapplication repository/marketplace 650 can be configured to provideone-click deployment and observability for the application lifecycle(e.g., from the cloud to the edge compute unit 430, and vice versa),obviating or reducing the need for cost and time intensive applicationand platform management as would conventionally be required.

In one illustrative example, application repository 650 can be used todeploy workloads into HCI through the global management console 620(e.g., a corresponding GUI of the global management console 620 for theapplication repository/marketplace 650). For instance, one or more AI/MLapplications can be selected from the application repository 650 (e.g.,selected from a plurality of ML or AI applications included in theapplication repository 650) for installation or deployment onto one ormore edge compute units 430, where the selection is made using globalmanagement console 620 and/or a GUI thereof. For instance, one or moreAI/ML applications can be obtained from the application repository 650and deployed to one or more edge compute units based on receiving arequest indicative of the one or more AI/ML applications that are to bedeployed. The request can be received using the global managementconsole 620 and/or a GUI thereof. The request can be indicative of aselection of one or more ML or AI applications qualified for deploymenton a particular edge compute unit(s) (e.g., one or more ML or AIapplications having minimum requirements that are met or exceeded by theparticular edge compute unit corresponding to the request).

In some embodiments, the request indicative of the selection of the oneor more qualified ML or AI applications can be a user request selectingfrom the application repository 650 (e.g., a manual request, user inputto a GUI of global management console 620 and/or a user input to a GUIfor the application repository 650, etc.). In some examples, the requestindicative of the selection of the one or more qualified ML or AIapplications can be automatically generated at the edge compute unit430, at the global management console 620, at the application repository650, etc. For example, an automatic request for deployment of an AI orML application from the application repository 650 to an edge computeunit 430 can be indicative of an automatically determined selection fromthe application repository 650. The automatic selection can be based on,in at least some examples, factors associated with the edge compute unit430, such as the particular configuration, capabilities, deploymentlocation (e.g., corresponding local edge site 402), deployment scenarioor deployment objectives, configured or available edge assets 410, typesof input or output data streams, existing model deployment instances435, 433, 435 on the edge compute unit 430, etc.

In some cases, the application repository 650 and/or global managementconsole 620 can be used to manage the lifecycle of deployed AI/ML appsfrom the application repository 650 (e.g., can be used to manage thelifecycle of the deployed user and platform AI/ML applications 655). Insome examples, the cloud-based application repository/marketplace 650can be used to implement management of the AI/ML applications 655 onbare metal (e.g., on bare metal services 617 of an edge compute unit430).

In some aspects, the platform services 602 can further include anapplication orchestration engine (not shown) that can be used for thedeployment of Kubernetes on the edge compute units 430. For instance, insome embodiments, the application orchestration engine can be used toprovide standalone Kubernetes clusters and Tanzu Kubernetes clusters onHCI. In some aspects, the application orchestration engine can be usedto provide automated Kubernetes cluster lifecycle management using helmand ArgoCD.

SDN Network Configuration—Provisioning, Management, Intelligent Routing

The platform services 602 can further include an SDN networkconfiguration service 660, which may be used to provide management ofnetworking functionality (e.g., SDN networking functionality) from thecloud. The SDN network configuration service 660 included in platformservices 602 can correspond to or be associated with the SDN networkprovisioning and management engine 665 included in the edge compute unitservices 605 implemented on each of the edge compute units 430. In oneillustrative example, the SDN networking can be used to enable disparateconnectivity options across different enterprise users' fleets of edgecompute units 430 and/or across the constituent edge compute units 430and local sites 402 of a single enterprise user's fleet of edge assets.

For instance, a network configuration manager (e.g., the cloud-based SDNnetwork configuration service 660 and/or edge-based SDN networkprovisioning and management engine 665) can be used to enable multipledifferent backhaul communication links to be established and configuredfor connection to a data network such as the internet. In particular,the network configuration manager can be used to enable multipledifferent backhauls to be configured to provide the internet backhaullink 440 depicted in FIG. 4 . For instance, the multiple backhauls canuse different communication modalities (e.g., such as wiredconnectivity, fiber connectivity, public or private 5G or other cellularnetwork connectivity, satellite internet constellation connectivity,etc.). The multiple backhauls may be used individually or may bemultiplexed together to provide internet backhaul communications over aplurality of backhaul links at the same time. The multiplexed backhaullinks may be of the same modality (e.g., all fiber backhaul links, allwireless cellular links, all satellite internet constellation links)and/or may be of mixed modalities (e.g., internet backhaul trafficmultiplexed over a combination of fiber, cellular, and/or satelliteinternet constellation communication links.

In some aspects, the network configuration manager can enable multipleinternet backhauls to be configured between the edge compute units430/local sites 402 and the platform services 602/cloud userenvironments 690/AI and ML training clusters 470. The multiple backhaulscan be configured based on leveraging network virtualization and remotemanagement of network assets to thereby expand the connectivity optionsat the edge (e.g., true edge). For instance, network virtualization andremote management of network assets can be configured or controlledthrough the global management console 620, to expand the connectivityoptions at the edge compute units 430 and corresponding local edge sitelocations 402 associated with an enterprise customer and/or theenterprise customer's fleet of managed edge assets registered to theplatform services 602. In some aspects, the use of networkvirtualization can enable customer data traffic, log/metrics/telemetrytraffic, and management/control plane traffic to be prioritizeddifferently within one or more (or both) of the local edge network 420at the local site 402 and within the one or more internet backhaul 440networks between the local site 402/edge compute unit 430 and theplatform services 602/cloud user environments 690.

In one illustrative example, the SDN network configuration service 660can have a corresponding GUI that is presented in global managementconsole 620, and can be used to perform SDN configuration and managementfor an SDN associated with one or more edge compute units 430, localsites 402, edge compute unit services 605, etc. As illustrated, the SDNnetwork configuration service 660 of the platform services 602 cancorrespond to an SDN network provisioning and management engine 665included in the edge compute unit services 605. In some examples, theSDN network configuration service 660 can receive one or more userinputs indicative of network configuration parameters, changes, updates,etc., and can transmit the SDN network configuration information to theSDN network provisioning and management engine 665 for application atthe edge compute unit 430.

In some aspects, the SDN network configuration service 660 can be acloud-based service of the platform services 602. The SDN networkprovisioning and management engine 665 of the edge compute unit services605 can be a locally implemented edge service (e.g., implemented on theedge compute unit 430) that utilizes cloud-based communication toreceive configuration information to be applied to SDN networkingassociated with the edge compute unit 430 and/or edge compute unitservices 605. In some cases, the SDN network provisioning and managementengine 665 can be responsible for network-level optimization andintelligent routing to/from the edge compute unit 430.

In some cases, the SDN network provisioning and management engine 665can be used to multiplex data transmission over multiple satelliteinternet constellation transceivers (e.g., uplink from edge compute unit430 to the cloud can be multiplexed over a first satellite internetconstellation link provided by a first satellite transceiver at thelocal site 402, a second satellite internet constellation link providedby a second satellite transceiver at the local site 402, a thirdsatellite internet constellation link provided by a third satellitetransceiver at the local site 402, . . . , etc.). In some cases, the SDNnetwork provisioning and management engine 665 and/or the cloud-basedSDN network configuration service 660 can be used to generate, collect,and/or display associated metrics for the SDN networking, satelliteinternet constellation connectivity and/or associated multiplexing, etc.In some aspects, the SDN network configuration service 660 and the SDNnetwork provisioning and management engine 665 can support multiple datanetwork modalities, including private 5G or other wireless cellularnetworks, wired fiber (e.g., fiber optic) connectivity, satelliteinternet constellation connectivity, etc.

Device/Asset Lifecycle Management & Fleet Management Daemon

The platform services 602 are depicted in FIG. 6 as further including adevice/asset lifecycle management (DLM) engine 670. The DLM engine 670can be used to perform tasks and operations such as provisioning,adding/removing, and managing connected assets associated with theplatform services 602. For instance, the DLM engine 670 can be used toperform asset management relating to the one or more edge compute units430 provided at the plurality of local sites 402, . . . , 402-N of FIG.4 . Connected assets managed by the DLM engine 670 can additionallyinclude various sensors and other assets, computing devices, etc.,provided at the edge and/or otherwise associated with an edge computeunit 430. For instance, the DLM engine 670 can be used to perform assetmanagement relating to the plurality of sensors or sensor packages thatare provided at a local site 402 and/or associated with generating inputsensor data used by an edge compute unit 430. For instance, the edgeassets 410 of FIG. 4 (e.g., autonomous robots 416, local site cameras414, environmental sensors 412, etc.) can each be managed by the DLMengine 670 of FIG. 6 .

In some examples, the DLM engine 670 can be a cloud-based component ormodule of the platform services 602. In one illustrative example, theDLM engine 670 can be associated with a corresponding GUI presented inthe global management console 620. For example, the DLM engine 670 cancorrespond to a GUI that is the same as or similar to the ‘Fleet Map’GUI illustrated in the example GUI 800 of FIG. 8 . In some aspects, theDLM engine 670 can additionally (or alternatively) correspond to an‘Assets’ GUI, shown as a selectable option in the left-hand sidebar menuof GUI 800 of FIG. 8 and GUI 900 of FIG. 9 .

In some cases, the DLM engine 670 GUI can display a listing or visualdepiction of the various assets that have been deployed, registered,provisioned, etc., for the enterprise user of platform services 602. Forinstance, the assets managed by DLM engine 670 can be separated,filtered, stored, etc., based on factors such as asset type, assetlocation, asset age, asset status, asset task or usage, etc.

In some embodiments, the functionality of DLM engine 670 can be providedby a DLM asset service and a DLM provisioning service that are bothincluded in DLM engine 670. For instance, the DLM asset service and theDLM provisioning service can be sub-services implemented by DLM engine670 in the platform services 602. The DLM asset service and DLMprovisioning service can both be cloud-based services. In some examples,the DLM asset service is a cloud-based service used to manage the assets(e.g., edge compute units 430, connected sensors, and/or other edgeassets 410 provided at a local site 402 edge location, etc.) belongingto an organization. In some examples, the DLM asset service can be acloud-based service configured to add assets to an organization, removeassets from an organization, list assets, manage additional propertieslike endpoints, etc. In some cases, the DLM asset service can have anexpanded schema to include a satellite internet constellation internalrepresentation within the scope of managed or monitored assets of theDLM asset service and/or DLM engine 670. In some cases, the satelliteinternet constellation internal representation can be implemented basedat least in part on the satellite edge connectivity management engine680 included in the platform services 602 (as will be described ingreater detail below).

The DLM provisioning service can be a separate cloud-based service thatis used to recognize assets belonging to an organization and registerthem as such. For instance, when a new edge asset, connected sensor, oredge compute unit, etc. is provided at a local site 402, the new edgeasset, connected sensor, or edge compute unit can initially connect toand communicate with the DLM provisioning service of the DLM engine 670(e.g., via the internet backhaul communication link 440 of FIG. 4 ).Based on the initial connection between the new edge device and the DLMprovisioning service of the DLM engine 670, provisioning can beperformed such that the new edge device can be registered to andassociated with the enterprise user or organization that operates thelocal site 402. In some embodiments, the DLM provisioning service canregister or provision assets as belonging to an organization based onhardcoding HCI assets as belonging to the particular organization. Insome embodiments, the DLM provisioning service can provision assetsusing certificates (CA), if turned on or enabled at the localcustomer/enterprise site (e.g., local site 402 of FIG. 4 ). In somecases, the DLM provisioning service can hardcode satellite internetconstellation assets as belonging to the organization. For instance, asatellite internet constellation transceiver coupled to or otherwise incommunication with the edge compute unit 430 (e.g., a satellite internetconstellation transceiver provided at or near the local site 402) can behardcoded as belonging to the organization using the DLM provisioningservice of the DLM engine 670. The satellite internet constellationtransceiver(s) may be the same as or similar to one or more of thesatellite transceivers 312, 314, 316, 321, 323, 325 of FIG. 3 , etc.

In some embodiments, the DLM engine 670 can further include a DLM cloudcontrol plane service (not shown). The DLM cloud control plane servicecan be used to implement a cloud component for the control planeresponsible for device management. For instance, the DLM cloud controlplane service can be used to deploy workloads, grab (e.g., retrieve orobtain) the live state of various HCI hosts (e.g., edge compute units430 or compute hardware/HCI hosts running thereon). In some embodiments,the DLM cloud control plane service can be used to send curated commandsand control indications to an edge compute unit 430, where the commandsmay be user-initiated, automatically or system initiated, or acombination of the two. For instance, a user input or configurationaction provided to a GUI of the global management console 620corresponding to the DLM engine 670 (or other component of platformservices 602) can be automatically translated into control planesignaling by the DLM cloud control plane service, and can be pushed tothe appropriate services of the edge compute unit 430 (e.g., translatedand pushed from the cloud-based DLM cloud control plane service withinplatform services 602, to the appropriate or corresponding one(s) of theedge compute unit services 605 running on the edge compute unit 430). Insome aspects, the DLM cloud control plane service can be implementedbased on a scalable design for control plane and additional managementAPIs.

In some examples, DLM engine 670 can further include or otherwise beassociated with an edge compute unit cloud control plane service (notshown). The edge compute unit cloud control plane service can beimplemented at the edge compute unit 430 (e.g., can be included in theedge compute unit services 605) and may provide a resident control planethat provides an interface into a given edge compute unit 430 from thecloud. For instance, the edge compute unit cloud control plane servicecan provide an interface from the global management console 620 (and/orother platform services 602) into a given edge compute unit 430. Theinterface into a given edge compute unit 430 can be mediated by the DLMcloud control plane service (on the cloud side) and the edge computeunit cloud control plane service (on the edge side). In some aspects,the edge compute unit cloud control plane service can be used toimplement REST endpoints for deploying applications (e.g., the user andplatform applications 655, deployed to the edge from the cloud-basedapplication repository 650), servicing curated commands, etc.

In some aspects, the DLM engine 670 of platform services 602 cancorrespond to or otherwise be associated with an edge-based fleetmanagement daemon 673 that is included in the edge compute unit services605 and/or deployed on the edge compute unit(s) 430. For instance, theedge-based fleet management daemon 673 can be configured to providenode-level data and metrics (where the node-level corresponds to thelevel of individual edge compute units 430). More generally, theedge-based fleet management daemon 673 can be configured to performcollection of vital statistics and data related to nodes/edge computeunits 430 registered with the platform services 602 and needed fordisplay, management, monitoring, or other interaction through the globalmanagement console 620. In some cases, the edge-based fleet managementdaemon 673 can additionally, or alternatively, be used to implement acoredump collector that is in communication with the cloud-based DLMengine 670.

As illustrated in FIG. 6 , the edge compute unit services 605 canfurther include connector services 677, which may also be referred to ascloud connector services 677. For instance, the cloud connector services677 can include a plurality of different cloud connectors for variouscloud platforms associated with the cloud user environments 690. Thecloud user environments 690 can be public or private clouds that areused to implement some (or all) of the platform services 602 of FIG. 6 ,the platform software stack 502 of FIG. 5 , the AI/ML training clusters470 of FIG. 4 , etc. In some embodiments, the cloud connector services677 can include a first cloud connector corresponding to a first publiccloud infrastructure, a second cloud connector corresponding to a secondpublic cloud infrastructure, a third cloud connector corresponding to afirst private cloud infrastructure, a fourth cloud connectorcorresponding to a second private cloud infrastructure, etc. The cloudconnector services 677 can be used to route or bridge trafficappropriately between the edge compute unit 430 and edge compute unitservices 605 to the appropriate one of the cloud user environments 690where the platform services 602 are implemented, provided, or located,etc. In some cases, the cloud connector services 677 can be used toconnect nodes of the edge compute unit 430/the edge compute unit 430itself to the appropriate user-specified cloud user environments 690. Insome cases, the cloud connector services 677 can be configured to uploadlogs related to platform AI/ML applications included in the user andplatform applications 655 running on the edge compute unit 430. In somecases, the cloud connector services 677 can be configured to providemechanisms for data plane interaction with the various third-party orprivate cloud providers associated with the cloud user environments 690.

Observer—Telemetry and Monitoring

The platform services 602 can further include the telemetry andmonitoring observer engine 630, which can correspond to or otherwise beassociated with the telemetry and monitoring stack 635 implemented onthe edge compute unit 430 among the edge compute unit services 605. Insome aspects, the observer can be used to provide hardware and criticalenvironment observability designed to be part of a comprehensive andunified software solution to simplify and streamline the management of acustomer' fleet of edge compute units 430 and associated edge assets410. For instance, the telemetry and monitoring observer engine 630and/or the telemetry and monitoring stack 635 can enable system-widevisibility, command, and control of the fleet's hardware systems (e.g.,the hardware systems of the edge compute units 430 and/or the hardwaresystems of the connected edge assets 410). The fleet's hardware systemsthat may be associated with, viewed, commanded, controlled, etc., by thetelemetry and monitoring observer engine 630 and/or the telemetry andmonitoring stack 635 can include, but are not limited to: powerdistribution systems or sub-systems, thermal management functionality,internal environmental control systems and functionalities, dataconnectivity (e.g., both backhaul and device), physical security systems(e.g., at local site 402, associated with edge compute unit 430,associated with connected edge assets 410, etc.).

In some aspects, the telemetry and monitoring stack 635 implemented onthe edge compute unit 430 (e.g., included in the edge compute unitservices 605) can include one or more cloud-based services orsub-services. In some aspects, the telemetry and monitoring stack 635can comprise a plurality of sub-services each running from the cloud,with the telemetry and monitoring stack 635 itself running from the edgecompute unit 430. In some embodiments, the telemetry and monitoringstack 635 can run at the edge and can include cloud-based services orsub-services configured to upload host-level and satellite internetconstellation level metrics to provide an observation view of telemetryand monitoring information from the cloud-based global managementconsole/portal 620.

For instance, the telemetry and monitoring stack 635 can include anetwork telemetry and monitoring service that runs in the cloud (e.g.,is a cloud-based service) and is configured to provide network usagestatistics corresponding to one or more of a local network 420associated with the edge compute unit 430, SDN networking associatedwith the edge compute unit 430 (e.g., SDN networking implemented basedon the SDN network configuration service 660 and SDN networkprovisioning and management engine 665), and/or internet backhaul 440associated with the edge compute unit 430 and cloud user environments690. In some cases, the cloud-based network telemetry and monitoringservice can be included in, associated with, etc., one or more of thecloud-based SDN network configuration service 660 included in theplatform services 602 and/or the edge-based SDN network provisioning andmanagement engine 665 included in the edge compute unit services 605deployed on the edge compute unit 430.

In some embodiments, the telemetry and monitoring stack 635 can includea satellite internet constellation telemetry and monitoring service thatruns in the cloud (e.g., is a cloud-based service) and is configured toprovide network usage statistics and satellite internet constellationmetrics corresponding to connectivity between the local site 402/edgecompute unit 430 and one or more bird (e.g., satellites) of thesatellite internet constellation. In some aspects, the cloud-basedsatellite internet constellation telemetry and monitoring service can beincluded in, associated with, etc., the satellite edge connectivitymanagement engine 680 included in the platform services 602.

In some cases, the telemetry and monitoring stack 635 can furtherinclude a critical environment telemetry and monitoring service runninglocally at the edge (e.g., on the edge compute unit 430/included in theedge compute unit services 605). The critical environment telemetry andmonitoring service can display data from one or more APIs associatedwith or provided with the containerized data center used to implementthe edge compute unit 430, and corresponding to telemetry and monitoringinformation for components within the edge compute unit 430 (e.g.,including ambient environmental parameters such as temperature orhumidity, power consumption, etc.; including monitoring parameters forvarious compute hardware included in the HPC engine 434 of edge computeunit 430; etc.). In some aspects, the critical environment telemetry andmonitoring service can upload HCI/satellite internet constellationmetrics to the cloud (e.g., platform services 602 and/or cloud userenvironments 690) for display in the global management console 620.

In some embodiments, the telemetry and monitoring stack 635 can furtherinclude a host level telemetry and monitoring (compute and storage)service running locally at the edge (e.g., on the edge compute unit430/included in the edge compute unit services 605). The host-leveltelemetry and monitoring (compute and storage) service can be used tocollect and/or display data from local edge hosts (e.g., edge computeunits 430) and/or Kubernetes clusters associated with the local edgecompute host units 430. The host-level telemetry and monitoring (computeand storage) service can upload HCI level host, virtual machine (VM),and/or Kubernetes data and metrics to the cloud (e.g., platform services602 and/or cloud user environments 690) for display in the globalmanagement console 620.

In some aspects, the telemetry and monitoring stack 635 can furtherinclude a network telemetry and monitoring service running locally atthe edge (e.g., on the edge compute unit 430/included in the edgecompute unit services 605) and configured to provide combined networkand satellite internet constellation connectivity metrics, network usagestatistics, etc. The network telemetry and monitoring service can uploadsatellite internet constellation metrics, HCI network utilizationmetrics, etc., to the cloud (e.g., platform services 602 and/or clouduser environments 690) for display in the global management console 620.

Satellite Internet Constellation—Edge Connectivity Management

In one illustrative example, the platform services 602 can include asatellite edge connectivity management engine 680. The satellite edgeconnectivity management engine 680 can be a cloud-based service orengine, and may correspond to a satellite internet constellationconnectivity module (e.g., edge module included in edge compute unit 430or deployed at the local site 402 and in communication with edge computeunit 430). In some cases, the satellite internet constellationconnectivity management engine 680 can comprise bundled softwareassociated with the satellite internet constellation edge module (e.g.,satellite internet constellation transceiver provided at the local site402/edge compute unit 430). In some embodiments, the satellite edgeconnectivity management engine 680 can be associated with acorresponding GUI of the global management console 620, where thecorresponding GUI runs or presents the bundled software associated withthe satellite internet constellation edge hardware module and/orpresents an interface or portal for management of the satellite internetconstellation internet connectivity. In some cases, the correspondingGUI of the global management console 620 for the satellite internetconstellation connectivity management engine 680 can display some (orall) of the satellite internet constellation metrics collected by thetelemetry and monitoring stack 635. In some cases, the satelliteinternet constellation metrics collected by the telemetry and monitoringstack 635 may be presented in global management console 620 in thededicated GUI corresponding to the satellite internet constellation edgeconnectivity management engine 680, can be presented in globalmanagement console 620 in a different dedicated GUI corresponding to thetelemetry and monitoring observer engine 630, and/or can be presentedacross both/multiple GUIs of the global management console 620.

In some aspects, the platform services 602 can further include asatellite internet constellation service backend module (not shown),either as a standalone engine/service and/or as a sub-engine/sub-serviceof the satellite edge connectivity management engine 680 included in theplatform services 602. For example, the satellite internet constellationservice backend module can be used to provide management and/ormonitoring of the service backend associated with using the satelliteinternet constellation to implement internet backhaul (e.g., internetbackhaul link 440 of FIG. 4 ) between the cloud user environments690/platform services 602 and the edge compute device 430/local site402. In some cases, the satellite internet constellation service backendmodule can include or provide a support ticket creation backend. Thesatellite internet constellation service backend module may additionallyprovide satellite internet constellation management and/or connectivitymanagement using one or more enterprise APIs associated with orconfigured for use with the satellite internet constellation. In someaspects, the satellite internet constellation service backend module caninclude the support ticket creation backend, and can additionallyinclude satellite internet constellation management functionalities(e.g., such as pause service, move service, change plans, etc.) usingcorresponding appropriate enterprise APIs for the satellite internetconstellation.

FIG. 7 is a diagram illustrating an example infrastructure andarchitecture 700 for implementing an edge computing system for ML and/orAI workloads, according to aspects of the present disclosure. Forinstance, FIG. 7 includes a global management platform 702 that can be acloud-based platform that can include one or more components that arethe same as or similar to corresponding components within the platformservices 602 of FIG. 6 and/or within the platform software stack 502 ofFIG. 5 . FIG. 7 additionally includes a plurality of edge compute units704 (e.g., a fleet of edge compute units 704), each of which may be thesame as or similar to the edge compute unit 430 of FIG. 4 and/or caninclude one or more components that are the same as or similar tocorresponding components within the edge compute unit services 605 ofFIG. 6 . In particular, each edge compute unit 704 of the plurality ofedge compute units can implement, include, or comprise an edge computeunit host 705, which can be the same as or similar to the edge computeunit services 605 of FIG. 6 .

For instance, a global management platform 702 can include theapplication repository 650 and global management console 620 of FIG. 6 ,in addition to the remote fleet management control plane 520 of FIG. 5 .The global management platform 702 can be a cloud-hosted and/oron-premises computing system that is remote from the respective localedge sites associated with various edge compute units 704 of the fleet(e.g., plurality) of edge compute units 704. For instance, globalmanagement platform 702 of FIG. 7 can be associated with one or more ofthe cloud-based AI/ML training clusters 470 of FIG. 4 , the cloud userenvironments 690 of FIG. 6 , etc.

The remote fleet management control plane 520 can include anorganization and onboarding service 722 that can be used to performorganization-specific tasks corresponding to an enterprise organization(e.g., enterprise user) of the global management platform 702 and/or theinfrastructure and architecture 700 for edge computing of ML and AIworkloads. For example, the onboarding service 722 can be used toonboard users for the enterprise organization, based on creating one ormore user accounts for the global management console 602 and/or thelocal management console 625 of FIG. 7 . The remote fleet managementcontrol plane 520 can additionally include a provisioning service 724that can be used to provision various edge assets associated with (e.g.,deployed by) the enterprise user. For instance, the provisioning service724 can be associated with provisioning satellite internet constellationtransceivers or connectivity units for the edge compute units 704, canbe associated with provisioning the edge compute units 704, can beassociated with provisioning one or more user devices (e.g., such as theuser device 795), can be associated with provisioning one or moreconnected edge assets 710-1, . . . , 710-N (e.g., which can be the sameas or similar to the connected edge assets 410 of FIG. 4 ), etc.

The remote fleet management control plane can include and/or can beassociated with one or more databases, such as a fleet datastore 747 anda metrics datastore 749. In some aspects, the fleet datastore 747 canstore data or information associated with the fleet of deployed edgecompute units 704. For instance, fleet datastore 747 can communicatewith one or more (or all) of the organization and onboarding service722, the provisioning service 724, the device lifecycle managementservice 670, etc. In some aspects, the fleet datastore 747 and/or themetrics datastore 749 can communicate with and be accessed by the globalmanagement console 620. For instance, global management console 620 canaccess and communicate with the metrics datastore 749 for metricsvisualization corresponding to one or more of the deployed edge computeunits 704 of the fleet (e.g., plurality) of deployed edge compute units704. In some embodiments, the fleet datastore 747 can include the localknowledge base/datastore 545 of FIG. 5 , described previously above.

As mentioned previously, the global management platform 702 can beassociated with and used to manage the deployment of a fleet of edgecompute units 704. The various edge compute units 704 can be deployed todifferent edge locations. For instance, one or more edge compute units704 can be deployed to each respective edge location that is associatedwith (e.g., is managed by and communicates with) the global managementplatform 702. As illustrated in the example of FIG. 7 , a first edgelocation may have four edge compute units deployed (e.g., left-mostdeployment shown in FIG. 7 ), a second edge location may have two edgecompute units deployed (e.g., center deployment shown in FIG. 7 ), athird edge location may have three edge compute units deployed (e.g.,right-most deployment shown in FIG. 7 ), etc. A greater or lesser numberof edge site locations can be utilized, each with a greater or lessernumber of edge compute units 704, without departing from the scope ofthe present disclosure.

Each edge compute unit can be associated with an edge compute unit host705, which is shown in the illustrative example of FIG. 7 ascorresponding to a single one of the plurality of edge compute units704. In particular, each edge compute unit 704 of the plurality of edgecompute units can implement, include, or comprise an edge compute unithost 705, which can be the same as or similar to the edge compute unitservices 605 of FIG. 6 , and/or can include or implement one or more ofthe components of edge compute unit 430 of FIG. 4 , etc. The edgecompute unit host 705 can include the local management console 625 ofFIG. 6 , which may be associated with a metrics datastore 742. Themetrics datastore 742 can be used to collect and store local telemetryand other metrics information generated and/or received at the edgecompute unit host 705 and/or corresponding local edge site of the edgecompute unit host 705. In some aspects, information included in thelocal metrics datastore 742 can be the same as or similar to at least aportion of the information included in the global management platform702 metrics datastore 749. In some cases, information included in thelocal metrics datastore 742 can be separate or disjoint from at least aportion of the information included in the global management platform702 metrics datastore 749.

In some examples, the local management console 625 can becommunicatively coupled with the local metrics datastore 742, and can beconfigured to provide metrics readout information and/or visualizationto one or more user devices 795 that are local to the same edge locationas the edge compute unit host 705 and that are authorized to access andinterface with the local management console 625 (e.g., access controland authorization may be implemented based on the organization andonboarding service 722 of the global management platform 702). The userdevices 795 can include various computing devices, including but notlimited to, desktop computers, laptop computers, tablet computers,smartphones, wearable computing devices, output devices or equipment,display devices or equipment, personal computing devices, mobilecomputing devices, portable hand units or terminals, display monitors,etc.) that may be present within or otherwise associated with the localedge site of the edge compute unit host 705.

The local management console 625 can additionally communicate with anedge observer engine 760, which can correspond to the telemetry andmonitoring stack 635 of the edge compute unit services 605 of FIG. 6 .In some embodiments, the edge observer engine 760 can be the same as orsimilar to the telemetry and monitoring stack 635 of FIG. 6 . The edgeobserver engine 760 can include a host-level telemetry service 737 and acritical environment monitoring service 739 (one or more, or both, ofwhich can be included in the telemetry and monitoring stack 635 of FIG.6 ). The critical environment monitoring service 739 can be used tomonitor environmental parameters of the edge compute unit 704/edgecompute unit host 705, such as temperature, humidity, airflow,vibrations, energy consumption, etc. The critical environment monitoringservice 739 can ingest, obtain, or otherwise access corresponding sensordata or sensor data streams from environmental monitoring sensors, whichcan include one or more (or all) of the environmental sensors 412 ofFIG. 4 . In some aspects, the edge observer engine 760 can additionallyinclude an application deployer 757, which can communicate with thecloud-based application repository 650 of the global management platform702 (e.g., the cloud-based application repository 650 of FIG. 6 ). Insome embodiments, log data from the edge observer engine 760 can betransmitted (e.g., as a log stream) from the edge observer engine 760 toa log archival agent 775 of a fleet management client 770 included inthe edge compute unit host 705. The log archival agent 775 can, in someaspects, parse and/or archive (e.g., store or transmit for storage) someor all of the log stream data received from and/or generated by the edgeobserver engine 760. For instance, the log archival agent 775 of thefleet management client 770 can transmit the log stream data receivedfrom and/or generated by the edge observer engine 760 to the cloud-basedmetrics datastore 749 of the global management platform 702, where thetransmitted log stream data from the cloud-based metrics datastore 749can be used for metrics visualization at or using the global managementconsole 620 (also of the global management platform 702).

In some aspects, the fleet management client 770 included in or deployedon the edge compute unit host 705 can be associated with the fleet ofdeployed edge compute units 704. For instance, the fleet managementclient 770 can associate the particular edge compute unit host 705 withthe corresponding additional edge compute unit hosts 705 that are alsoincluded in the same fleet. In some aspects, the fleet management client770 can be used to coordinate and implement distributed operations(e.g., computational operations, such as finetuning, retraining, etc.,of one or more AI/ML models) across multiple edge compute units 704 ofthe fleet. For instance, in one illustrative example, distributedfinetuning or retraining of an AI/ML model across multiple edge computeunits 704 be orchestrated by a respective fleet management client 770that is implemented at or by each of the multiple edge compute units704. As illustrated, the fleet management client 770 can include thefleet management daemon 673 described above with respect to FIG. 6 . Thefleet management daemon 673 of the fleet management client 770 providedon each edge compute unit host 705 can communicate with the devicelifecycle management service 670 of the remote fleet management controlplane 520 implemented in the global management platform 702. In someaspects, the fleet management daemon 673 of the fleet management client770 provided on each edge compute unit host 705 can communicate with theremote fleet management control plane 520, the global management console620, and/or various other component and services within the globalmanagement platform 702 of FIG. 7 .

In some aspects, the edge compute unit host 705 can communicate with aplurality of connected edge assets 710-1, . . . , 710-N. As notedpreviously, the connected edge assets 710-1, . . . , 710-N can be thesame as or similar to the connected edge assets 410 of FIG. 4 , and caninclude various sensors, computing devices, etc., that are associatedwith an edge deployment location of the edge compute unit host 705. Forinstance, the connected edge assets 710-1, . . . , 710-N incommunication with the edge compute unit host 705 can include, but arenot limited to, one or more of sensors such as cameras, thermal imagers,lidars, radars, gyroscopes, accelerometers, vibrometers, acousticsensors or acoustic sensor arrays, sonar sensors or sonar sensor arrays,pressure sensors, temperature sensors, X-ray units, magnetic resonanceimaging (MRI) units, electroencephalogram (EEG) units, electrocardiogram(ECG) units, inertial navigation system (INS) units, inertialmeasurement units (IMUs), GPS modules, positioning system modules,compass sensors, directional sensors, magnetic field sensors, roboticplatforms, robotic units, robotic devices, etc., among various others.In some aspects, the connected edge assets 710-1, . . . , 710-Nassociated with the edge compute unit host 705 can include all devicesconnected to edge compute units that have local ingress and egress ofdata.

Edge AI/ML Monitoring and Management Platform—Management Console GUIExamples

FIG. 8 is a diagram illustrating an example graphical user interface(GUI) 800 of a global management console associated with assetmanagement and telemetry observation for a fleet of edge compute unitsof an edge computing system for ML and/or AI workloads, in accordancewith some examples. For instance, the GUI 800 can correspond to a“Remote Fleet Management” GUI of the global management platform 702 ofFIG. 7 . In some aspects, the Remote Fleet Management GUI 800 of FIG. 8can be presented using the Global Management Console 620 of FIG. 6 andFIG. 7 . In some cases, the Remote Fleet Management GUI 800 of FIG. 8can additionally, or alternatively, be associated with the remote fleetmanagement control plane 520 of FIG. 5 and FIG. 7 .

The Remote Fleet Management GUI 800 can include user interface elementscorresponding to different platform services. For instance, the userinterface elements (e.g., presented in the left-hand column of theRemote Fleet Management GUI 800) can include, but are not limited to, anowned assets 852 UI element (e.g., corresponding to a display of theowned assets associated with or registered to an enterprise user'sfleet); an edge compute units 852 UI element (e.g., corresponding to adisplay of the edge compute units 810, . . . , 810-N associated with orregistered to an enterprise user's fleet); a deployed AI/ML applications856 UI element (e.g., corresponding to a display of the selection ofAI/ML applications deployed to the enterprise user's fleet of edgecompute units); a deployable AI/ML application repository 858 UI element(e.g., corresponding to a display of available AI/ML applications thatcan be deployed from the repository 550 of FIG. 5 or the repository 650of FIGS. 6 and 7 ); a users and roles 862 UI element (e.g.,corresponding to a display of registered users and roles for interactingwith the remote fleet management system and/or the global managementconsole 620 of FIGS. 6 and 7 ); a remote monitoring 864 UI element(e.g., corresponding to a display of remote monitoring and telemetrydata, such as data from the edge observer 760 of FIG. 7 and deployed oneach edge compute unit of the enterprise user's fleet); and a security866 UI element (e.g., corresponding to a display of securityinformation, alerts, monitoring information, etc.).

A plurality of user interface elements presented in the horizontal rowat the top of the example Remote Fleet Management GUI 800 can be used tofilter a display of the fleet map 802 information within the GUI 800.For instance, the edge compute units UI element 810 can be used toselect for display the plurality (e.g., fleet) of edge compute units810-1, . . . , 810-N included in the fleet map 802. The satellites UIelement 832 can be used to select for display the satellite transceiverunits 822 included in the fleet map 802 and associated with particularones of the edge compute units 810-1, . . . , 810-N. The cameras UIelement 834 can be used to select for display the cameras 824 includedin the fleet map 802 and associated with particular ones of the edgecompute units 810-1, . . . , 810-N. The sensors UI element 836 can beused to select for display the sensors 826 included in the fleet map 802and associated with particular ones of the edge compute units 810-1, . .. , 810-N. The robots UI element 838 can be used to select for displaythe robotic units 828 included in the fleet map 802 and associated withparticular ones of the edge compute units 810-1, . . . , 810-N. Thedrones UI element 839 can be used to select for display the drone units829 included in the fleet map 802 and associated with particular ones ofthe edge compute units 810-1, . . . , 810-N. The vehicles UI element 837can be used to select for display the vehicle units 827 included in thefleet map 802 and associated with particular ones of the edge computeunits 810-1, . . . , 810-N.

In some aspects, the cameras 824, satellite transceivers 822, sensors826, robotic units 828, drones 829, and vehicles 827 can be included ina set of connected assets 820 of the fleet map 802. The connected assets820 of FIG. 8 can be the same as or similar to one or more of theconnected assets 410 of FIG. 4 and/or the connected assets 710-1, . . ., 710-N of FIG. 7 . In some aspects, each connected assets 820 instance(e.g., each individual asset) may be associated with a respective one ofthe edge compute units 810-1, . . . , 810-N. In some embodiments, one ormore connected assets 820 instances (e.g., one or more individual assetswithin the fleet map 802) may be provided at an edge location withoutbeing associated with a respective one of the edge compute units 810-01,. . . , 810-N. For instance, an edge location may include a satellitetransceiver 822 and one or more connected sensors 826, without includingan edge compute unit 810 at the edge location. In another example, anedge location may include one or more satellite transceivers 822configured to act as a communications relay for various other edgelocations and edge compute units 810, without include an edge computeunit 810 at the edge location of the relay satellite transceiver(s) 822,etc.

In some aspects, each asset of the connected assets 820 included withinthe fleet map 802 can be associated with a corresponding health statusinformation, as shown in the example Remote Fleet Management GUI 800 ofFIG. 8 . For instance, the corresponding health status information foreach respective asset of the plurality of connected assets 820 caninclude a selection between one of a ‘Healthy’ status, an ‘Attention’status, or a ‘Critical’ status. In some aspects, the correspondinghealth status information for each respective asset of the plurality ofconnected assets 820 can be selected for display within the GUI 800using the corresponding health status selection UI elements 835-1,835-2, and 835-3. For instance, the healthy assets UI selection element835-1 can trigger display of the subset of assets within the pluralityof connected assets 820 that are currently associated with the ‘Healthy’status. The attention assets UI selection element 835-2 can triggerdisplay of the subset of assets within the plurality of connected assets820 that are currently associated with the ‘Attention’ status. Thecritical assets UI selection element 835-3 can trigger display of thesubset of assets within the plurality of connected assets 820 that arecurrently associated with the ‘Critical’ status.

FIG. 9 is a diagram illustrating another example GUI 900 of a globalmanagement console associated with asset management and telemetryobservation for a fleet of edge compute units of an edge computingsystem for ML and/or AI workloads, in accordance with some examples. Inone illustrative example, the GUI 900 of FIG. 9 can correspond toselection of the edge compute units UI element 854 of the Remote FleetManagement GUI 800 of FIG. 8 (e.g., and the GUI 800 of FIG. 8 cancorrespond to selection of the owned assets UI element 852). The GUI 900of FIG. 9 can be indicative of drill-down or detailed informationcorresponding to a selected edge compute unit of the plurality of edgecompute units 810-1, . . . , 810-N of the fleet map 802 of FIG. 8 . Forinstance, the selected edge compute unit can correspond to a specificlocation (e.g., Houston, TX) of the edge site deployment location and/orcan be identified in the GUI 900 based on a unique name or identifier ofthe selected edge compute unit (e.g., GAL-TX-R76A). The GUI 900 caninclude a UI element 910 for displaying host metrics information of theselected edge compute unit; a UI element 912 for displaying power andenvironmental metrics information of the selected edge compute unit; anda UI element 914 for displaying app metrics information of the selectededge compute unit. In some aspects, the app metrics UI element 914 cancorrespond to application metrics associated with a subset of the AI/MLapplications 856 of FIG. 8 , where the subset comprises the particularAI/ML applications that are deployed on the selected edge compute unit.

In one illustrative example, the GUI 900 of FIG. 9 can correspond to theselection of the host metrics UI element 910. The host metrics displayof GUI 900 can include a CPU utilization information 920, which can beindicative of CPU utilization information (e.g., a percentage) of theselected edge compute unit relative to a total CPU cores available 925for the selected edge compute unit. The host metrics display of GUI 900can include a memory utilization information 930, which can beindicative of memory utilization information (e.g., a percentage) of theselected edge compute unit relative to a total memory available 935 forthe selected edge compute unit. The host metrics display of GUI 900 caninclude a storage utilization information 940, which can be indicativeof storage utilization information (e.g., a percentage) of the selectededge compute unit relative to a total storage available 935 for theselected edge compute unit. The host metrics display of GUI 900 caninclude a GPU utilization information 970, which can be indicative ofGPU utilization information (e.g., a percentage) of the selected edgecompute unit relative to a total GPU TFLOPS available 975 for theselected edge compute unit.

In some embodiments, some (or all) of the host utilization information920, 930, 940, 970 can be displayed within GUI 900 in combination with acorresponding utilization graph and/or historical utilizationinformation at the selected edge compute unit. For instance, the CPUutilization information 920 can correspond to a CPU utilization graph928 and/or other time-based or historical CPU utilization information atthe selected edge compute unit. The memory utilization information 930can correspond to a memory utilization graph 938 and/or other time-basedor historical memory utilization information at the selected edgecompute unit. The storage utilization information 940 can correspond toa storage utilization graph 948 and/or other time-based or historicalstorage utilization information at the selected edge compute unit. TheGPU utilization information 970 can correspond to a GPU utilizationgraph 978 and/or other time-based or historical GPU utilizationinformation at the selected edge compute unit.

In some examples, the systems and techniques described herein can beimplemented or otherwise performed by a computing device, apparatus, orsystem. In one example, the systems and techniques described herein canbe implemented or performed by a computing device or system having thecomputing device architecture 1000 of FIG. 10 . The computing device,apparatus, or system can include any suitable device, such as a mobiledevice (e.g., a mobile phone), a desktop computing device, a tabletcomputing device, a wearable device (e.g., a VR headset, an AR headset,AR glasses, a network-connected watch or smartwatch, or other wearabledevice), a server computer, an autonomous vehicle or computing device ofan autonomous vehicle, a robotic device, a laptop computer, a smarttelevision, a camera, and/or any other computing device with theresource capabilities to perform the processes described herein. In somecases, the computing device or apparatus may include various components,such as one or more input devices, one or more output devices, one ormore processors, one or more microprocessors, one or moremicrocomputers, one or more cameras, one or more sensors, and/or othercomponent(s) that are configured to carry out the steps of processesdescribed herein. In some examples, the computing device may include adisplay, a network interface configured to communicate and/or receivethe data, any combination thereof, and/or other component(s). Thenetwork interface may be configured to communicate and/or receiveInternet Protocol (IP) based data or other type of data.

The components of the computing device can be implemented in circuitry.For example, the components can include and/or can be implemented usingelectronic circuits or other electronic hardware, which can include oneor more programmable electronic circuits (e.g., microprocessors,graphics processing units (GPUs), digital signal processors (DSPs),central processing units (CPUs), and/or other suitable electroniccircuits), and/or can include and/or be implemented using computersoftware, firmware, or any combination thereof, to perform the variousoperations described herein.

Processes described herein can comprise a sequence of operations thatcan be implemented in hardware, computer instructions, or a combinationthereof. In the context of computer instructions, the operationsrepresent computer-executable instructions stored on one or morecomputer-readable storage media that, when executed by one or moreprocessors, perform the recited operations. Generally,computer-executable instructions include routines, programs, objects,components, data structures, and the like that perform particularfunctions or implement particular data types. The order in which theoperations are described is not intended to be construed as alimitation, and any number of the described operations can be combinedin any order and/or in parallel to implement the processes.

Additionally, processes described herein may be performed under thecontrol of one or more computer systems configured with executableinstructions and may be implemented as code (e.g., executableinstructions, one or more computer programs, or one or moreapplications) executing collectively on one or more processors, byhardware, or combinations thereof. As noted above, the code may bestored on a computer-readable or machine-readable storage medium, forexample, in the form of a computer program comprising a plurality ofinstructions executable by one or more processors. The computer-readableor machine-readable storage medium may be non-transitory.

FIG. 10 illustrates an example computing device architecture 1000 of anexample computing device which can implement the various techniquesdescribed herein. In some examples, the computing device can include amobile device, a wearable device, an extended reality device (e.g., avirtual reality (VR) device, an augmented reality (AR) device, or amixed reality (MR) device), a personal computer, a laptop computer, avideo server, a vehicle (or computing device of a vehicle), or otherdevice. The components of computing device architecture 1000 are shownin electrical communication with each other using connection 1005, suchas a bus. The example computing device architecture 1000 includes aprocessing unit (CPU or processor) 1010 and computing device connection1005 that couples various computing device components includingcomputing device memory 1015, such as read only memory (ROM) 1020 andrandom-access memory (RAM) 1025, to processor 1010.

Computing device architecture 1000 can include a cache of high-speedmemory connected directly with, in close proximity to, or integrated aspart of processor 1010. Computing device architecture 1000 can copy datafrom memory 1015 and/or the storage device 1030 to cache 1012 for quickaccess by processor 1010. In this way, the cache can provide aperformance boost that avoids processor 1010 delays while waiting fordata. These and other engines can control or be configured to controlprocessor 1010 to perform various actions. Other computing device memory1015 may be available for use as well. Memory 1015 can include multipledifferent types of memory with different performance characteristics.Processor 1010 can include any general-purpose processor and a hardwareor software service, such as service 1 1032, service 2 1034, and service3 1036 stored in storage device 1030, configured to control processor1010 as well as a special-purpose processor where software instructionsare incorporated into the processor design. Processor 1010 may be aself-contained system, containing multiple cores or processors, a bus,memory controller, cache, etc. A multi-core processor may be symmetricor asymmetric.

To enable user interaction with the computing device architecture 1000,input device 1045 can represent any number of input mechanisms, such asa microphone for speech, a touch-sensitive screen for gesture orgraphical input, keyboard, mouse, motion input, speech and so forth.Output device 1035 can also be one or more of a number of outputmechanisms known to those of skill in the art, such as a display,projector, television, speaker device, etc. In some instances,multimodal computing devices can enable a user to provide multiple typesof input to communicate with computing device architecture 1000.Communication interface 1040 can generally govern and manage the userinput and computing device output. There is no restriction on operatingon any particular hardware arrangement and therefore the basic featureshere may easily be substituted for improved hardware or firmwarearrangements as they are developed.

Storage device 1030 is a non-volatile memory and can be a hard disk orother types of computer readable media which can store data that areaccessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,random access memories (RAMs) 1025, read only memory (ROM) 1020, andhybrids thereof. Storage device 1030 can include services 1032, 1034,1036 for controlling processor 1010. Other hardware or software modulesor engines are contemplated. Storage device 1030 can be connected to thecomputing device connection 1005. In one aspect, a hardware module thatperforms a particular function can include the software component storedin a computer-readable medium in connection with the necessary hardwarecomponents, such as processor 1010, connection 1005, output device 1035,and so forth, to carry out the function.

Aspects of the present disclosure are applicable to any suitableelectronic device (such as security systems, smartphones, tablets,laptop computers, vehicles, drones, or other devices) including orcoupled to one or more active depth sensing systems. While describedbelow with respect to a device having or coupled to one light projector,aspects of the present disclosure are applicable to devices having anynumber of light projectors and are therefore not limited to specificdevices.

The term “device” is not limited to one or a specific number of physicalobjects (such as one smartphone, one controller, one processing systemand so on). As used herein, a device may be any electronic device withone or more parts that may implement at least some portions of thisdisclosure. While the below description and examples use the term“device” to describe various aspects of this disclosure, the term“device” is not limited to a specific configuration, type, or number ofobjects. Additionally, the term “system” is not limited to multiplecomponents or specific aspects. For example, a system may be implementedon one or more printed circuit boards or other substrates and may havemovable or static components. While the below description and examplesuse the term “system” to describe various aspects of this disclosure,the term “system” is not limited to a specific configuration, type, ornumber of objects.

Specific details are provided in the description above to provide athorough understanding of the aspects and examples provided herein.However, it will be understood by one of ordinary skill in the art thatthe aspects may be practiced without these specific details. For clarityof explanation, in some instances the present technology may bepresented as including individual functional blocks including functionalblocks comprising devices, device components, steps or routines in amethod embodied in software, or combinations of hardware and software.Additional components may be used other than those shown in the figuresand/or described herein. For example, circuits, systems, networks,processes, and other components may be shown as components in blockdiagram form in order not to obscure the aspects in unnecessary detail.In other instances, well-known circuits, processes, algorithms,structures, and techniques may be shown without unnecessary detail inorder to avoid obscuring the aspects.

Individual aspects may be described above as a process or method whichis depicted as a flowchart, a flow diagram, a data flow diagram, astructure diagram, or a block diagram. Although a flowchart may describethe operations as a sequential process, many of the operations can beperformed in parallel or concurrently. In addition, the order of theoperations may be re-arranged. A process is terminated when itsoperations are completed, but could have additional steps not includedin a figure. A process may correspond to a method, a function, aprocedure, a subroutine, a subprogram, etc. When a process correspondsto a function, its termination can correspond to a return of thefunction to the calling function or the main function.

Processes and methods according to the above-described examples can beimplemented using computer-executable instructions that are stored orotherwise available from computer-readable media. Such instructions caninclude, for example, instructions and data which cause or otherwiseconfigure a general-purpose computer, special purpose computer, or aprocessing device to perform a certain function or group of functions.Portions of computer resources used can be accessible over a network.The computer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, firmware,source code, etc.

The term “computer-readable medium” includes, but is not limited to,portable or non-portable storage devices, optical storage devices, andvarious other mediums capable of storing, containing, or carryinginstruction(s) and/or data. A computer-readable medium may include anon-transitory medium in which data can be stored and that does notinclude carrier waves and/or transitory electronic signals propagatingwirelessly or over wired connections. Examples of a non-transitorymedium may include, but are not limited to, a magnetic disk or tape,optical storage media such as flash memory, memory or memory devices,magnetic or optical disks, flash memory, USB devices provided withnon-volatile memory, networked storage devices, compact disk (CD) ordigital versatile disk (DVD), any suitable combination thereof, amongothers. A computer-readable medium may have stored thereon code and/ormachine-executable instructions that may represent a procedure, afunction, a subprogram, a program, a routine, a subroutine, a module, anengine, a software package, a class, or any combination of instructions,data structures, or program statements. A code segment may be coupled toanother code segment or a hardware circuit by passing and/or receivinginformation, data, arguments, parameters, or memory contents.Information, arguments, parameters, data, etc. may be passed, forwarded,or transmitted via any suitable means including memory sharing, messagepassing, token passing, network transmission, or the like.

In some aspects the computer-readable storage devices, mediums, andmemories can include a cable or wireless signal containing a bit streamand the like. However, when mentioned, non-transitory computer-readablestorage media expressly exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

Devices implementing processes and methods according to thesedisclosures can include hardware, software, firmware, middleware,microcode, hardware description languages, or any combination thereof,and can take any of a variety of form factors. When implemented insoftware, firmware, middleware, or microcode, the program code or codesegments to perform the necessary tasks (e.g., a computer-programproduct) may be stored in a computer-readable or machine-readablemedium. A processor(s) may perform the necessary tasks. Typical examplesof form factors include laptops, smart phones, mobile phones, tabletdevices or other small form factor personal computers, personal digitalassistants, rackmount devices, standalone devices, and so on.Functionality described herein also can be embodied in peripherals oradd-in cards. Such functionality can also be implemented on a circuitboard among different chips or different processes executing in a singledevice, by way of further example.

The instructions, media for conveying such instructions, computingresources for executing them, and other structures for supporting suchcomputing resources are example means for providing the functionsdescribed in the disclosure.

In the foregoing description, aspects of the application are describedwith reference to specific aspects thereof, but those skilled in the artwill recognize that the application is not limited thereto. Thus, whileillustrative aspects of the application have been described in detailherein, it is to be understood that the inventive concepts may beotherwise variously embodied and employed, and that the appended claimsare intended to be construed to include such variations, except aslimited by the prior art. Various features and aspects of theabove-described application may be used individually or jointly.Further, aspects can be utilized in any number of environments andapplications beyond those described herein without departing from thebroader spirit and scope of the specification. The specification anddrawings are, accordingly, to be regarded as illustrative rather thanrestrictive. For the purposes of illustration, methods were described ina particular order. It should be appreciated that in alternate aspects,the methods may be performed in a different order than that described.

One of ordinary skill will appreciate that the less than (“<”) andgreater than (“>”) symbols or terminology used herein can be replacedwith less than or equal to (“≤”) and greater than or equal to (“≥”)symbols, respectively, without departing from the scope of thisdescription.

Where components are described as being “configured to” perform certainoperations, such configuration can be accomplished, for example, bydesigning electronic circuits or other hardware to perform theoperation, by programming programmable electronic circuits (e.g.,microprocessors, or other suitable electronic circuits) to perform theoperation, or any combination thereof.

The phrase “coupled to” refers to any component that is physicallyconnected to another component either directly or indirectly, and/or anycomponent that is in communication with another component (e.g.,connected to the other component over a wired or wireless connection,and/or other suitable communication interface) either directly orindirectly.

Claim language or other language reciting “at least one of” a set and/or“one or more” of a set indicates that one member of the set or multiplemembers of the set (in any combination) satisfy the claim. For example,claim language reciting “at least one of A and B” or “at least one of Aor B” means A, B, or A and B. In another example, claim languagereciting “at least one of A, B, and C” or “at least one of A, B, or C”means A, B, C, or A and B, or A and C, or B and C, or A and B and C. Thelanguage “at least one of” a set and/or “one or more” of a set does notlimit the set to the items listed in the set. For example, claimlanguage reciting “at least one of A and B” or “at least one of A or B”can mean A, B, or A and B, and can additionally include items not listedin the set of A and B.

The various illustrative logical blocks, modules, engines, circuits, andalgorithm steps described in connection with the aspects disclosedherein may be implemented as electronic hardware, computer software,firmware, or combinations thereof. To clearly illustrate thisinterchangeability of hardware and software, various illustrativecomponents, blocks, modules, engines, circuits, and steps have beendescribed above generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. Skilled artisans may implement the described functionality invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the present application.

The techniques described herein may also be implemented in electronichardware, computer software, firmware, or any combination thereof. Suchtechniques may be implemented in any of a variety of devices such asgeneral purposes computers, wireless communication device handsets, orintegrated circuit devices having multiple uses including application inwireless communication device handsets and other devices. Any featuresdescribed as modules or components may be implemented together in anintegrated logic device or separately as discrete but interoperablelogic devices. If implemented in software, the techniques may berealized at least in part by a computer-readable data storage mediumcomprising program code including instructions that, when executed,performs one or more of the methods described above. Thecomputer-readable data storage medium may form part of a computerprogram product, which may include packaging materials. Thecomputer-readable medium may comprise memory or data storage media, suchas random-access memory (RAM) such as synchronous dynamic random-accessmemory (SDRAM), read-only memory (ROM), non-volatile random-accessmemory (NVRAM), electrically erasable programmable read-only memory(EEPROM), FLASH memory, magnetic or optical data storage media, and thelike. The techniques additionally, or alternatively, may be realized atleast in part by a computer-readable communication medium that carriesor communicates program code in the form of instructions or datastructures and that can be accessed, read, and/or executed by acomputer, such as propagated signals or waves.

The program code may be executed by a processor, which may include oneor more processors, such as one or more digital signal processors(DSPs), general purpose microprocessors, an application specificintegrated circuits (ASICs), field programmable logic arrays (FPGAs), orother equivalent integrated or discrete logic circuitry. Such aprocessor may be configured to perform any of the techniques describedin this disclosure. A general-purpose processor may be a microprocessor;but in the alternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration. Accordingly, the term “processor,” as used herein mayrefer to any of the foregoing structure, any combination of theforegoing structure, or any other structure or apparatus suitable forimplementation of the techniques described herein.

Illustrative aspects of the disclosure include:

Aspect 1. A method comprising: obtaining a pre-trained machine learningmodel corresponding to a request; transmitting the pre-trained machinelearning model to an edge compute unit associated with the request,wherein the edge compute unit is deployed to an edge location andconfigured to perform inference using the pre-trained machine learningmodel and one or more sensor data streams obtained at the edge location;receiving, from the edge compute unit, one or more batch uploads ofinformation associated with inference performed by the edge compute unitand using the pre-trained machine learning model; generating one or moreupdated machine learning models corresponding to the pre-trained machinelearning model, based on using the batch uploads of information from theedge compute unit to retrain or finetune the pre-trained machinelearning model; and transmitting the updated machine learning model tothe edge compute unit, wherein transmission of the updated machinelearning model is responsive to receiving the one or more batch uploadsof information.

Aspect 2. The method of Aspect 1, wherein: the request is indicative ofa selection of a machine learning (ML) or artificial intelligence (AI)application qualified for deployment on the edge compute unit; and theML or AI application qualified for deployment on the edge compute unitis selected from a repository including a plurality of ML or AIapplications.

Aspect 3. The method of Aspect 2, wherein the selected ML or AIapplication utilizes one or more pre-trained machine learning models,including the pre-trained machine learning model corresponding to therequest.

Aspect 4. The method of any of Aspects 2 to 3, wherein the ML or AIapplication is qualified for deployment on the edge compute unit basedon one or more of: a comparison between a computational hardwareconfiguration requirement of the ML or AI application and a respectivecomputational hardware deployment of the edge compute unit; or acomparison between a connected edge asset requirement of the ML or AIapplication and a respective connected edge asset deployment associatedwith the edge compute unit.

Aspect 5. The method of Aspect 4, wherein the connected edge assetrequirement of the ML or AI application is indicative of one or moretypes of input data required for the ML or AI application, and whereinthe one or more types of input data required correspond to a set ofconnected edge asset types.

Aspect 6. The method of any of Aspects 4 to 5, wherein: the connectededge asset requirement is indicative of one or more different modalitiesof the one or more sensor data streams for inference using thepre-trained machine learning model; and the comparison between theconnected edge asset requirement and the respective connected edge assetdeployment associated with the edge compute unit is based on adetermination of one or more sensor data stream modalities available atthe edge compute unit.

Aspect 7. The method of any of Aspects 4 to 6, wherein the connectededge asset requirement is indicative of one or more sensor types forgenerating the one or more sensor data streams obtained at the edgelocation.

Aspect 8. The method of any of Aspects 4 to 7, wherein the connectededge asset requirement is indicative of one or more robotic assetsassociated with obtaining the one or more sensor data streams at theedge location.

Aspect 9. The method of any of Aspects 2 to 8, wherein: the ML or AIapplication is configured to generate as output one or more controlcommands for a respective edge device type; and the ML or AI applicationis qualified for deployment on the edge compute unit based on adetermination that the edge compute unit is communicatively coupled toat least one edge device of the respective edge device type and providedat the edge location.

Aspect 10. The method of Aspect 9, wherein: the ML or AI application isconfigured to generate as output routing instructions for one or moredrones or robotic units provided at the edge location andcommunicatively coupled to the edge compute unit.

Aspect 11. The method of any of Aspects 1 to 10, wherein: the edgecompute unit comprises an edge infrastructure node having self-containedstorage hardware, computational hardware, and connectivity hardwarewithin a single housing.

Aspect 12. The method of Aspect 11, wherein the edge infrastructure nodeis a containerized edge data center unit.

Aspect 13. The method of any of Aspects 1 to 12, further comprising:receiving the request from the edge compute unit, wherein the request isreceived from the edge compute unit using a satellite internetconstellation connectivity link associated with one or more satellitetransceivers of the edge compute unit.

Aspect 14. The method of any of Aspects 1 to 13, wherein the pre-trainedmachine learning model is transmitted to the edge compute unit usingsatellite internet constellation connectivity.

Aspect 15. The method of any of Aspects 1 to 14, wherein the pre-trainedmachine learning model is transmitted to the edge compute unit from acloud deployment of a plurality of ML or AI training clusters.

Aspect 16. The method of Aspect 15, wherein: obtaining the pre-trainedmachine learning model comprises training a baseline machine learningmodel using the cloud deployment of the plurality of ML or AI trainingclusters; and training is performed based at least in part on trainingdata corresponding to one or more sensor data streams at the edgelocation and associated with the edge compute unit.

Aspect 17. The method of any of Aspects 1 to 16, wherein the one or morebatch uploads of information are indicative of performance metricsassociated with the inference performed by the edge compute unit, andwherein the method further comprises: analyzing the performance metricsassociated with the inference performed by the edge compute unit; andbased on a determination that inference performance of the edge computeunit is below a configured threshold, generating and transmitting theupdated machine learning model to the edge compute unit.

Aspect 18. The method of Aspect 17, further comprising: analyzing theperformance metrics associated with the inference performed by the edgecompute unit; and based on a determination that inference performance ofthe edge compute unit is below a configured threshold, transmitting tothe edge compute unit a command to perform one or more of modelretraining or model finetuning for the pre-trained machine learningmodel.

Aspect 19. The method of Aspect 18, further comprising transmitting, tothe edge compute unit, supplemental information for performing the oneor more of model retraining or model finetuning.

Aspect 20. The method of Aspect 19, wherein: the supplementalinformation for model retaining or model finetuning is generated basedon respective batch upload information received from one or moreadditional edge compute units different from the edge compute unit; andthe one or more additional edge compute units are configured to performinference using the same pre-trained machine learning model.

Aspect 21. An apparatus comprising means for performing any of theoperations of Aspects 1 to 20.

Aspect 22. A non-transitory computer-readable storage medium havingstored thereon instructions which, when executed by one or moreprocessors, cause the one or more processors to perform any of theoperations of Aspects 1 to 20.

Aspect 23. An apparatus, comprising: at least one memory; and at leastone processor coupled to the at least one memory, the at least oneprocessor configured to: obtain a pre-trained machine learning modelcorresponding to a request; transmit the pre-trained machine learningmodel to an edge compute unit associated with the request, wherein theedge compute unit is deployed to an edge location and configured toperform inference using the pre-trained machine learning model and oneor more sensor data streams obtained at the edge location; receive, fromthe edge compute unit, one or more batch uploads of informationassociated with inference performed by the edge compute unit and usingthe pre-trained machine learning model; generate one or more updatedmachine learning models corresponding to the pre-trained machinelearning model, based on using the batch uploads of information from theedge compute unit to retrain or finetune the pre-trained machinelearning model; and transmit the updated machine learning model to theedge compute unit, wherein transmission of the updated machine learningmodel is responsive to receiving the one or more batch uploads ofinformation.

Aspect 24. The apparatus of Aspect 23, wherein: the request isindicative of a selection of a machine learning (ML) or artificialintelligence (AI) application qualified for deployment on the edgecompute unit; and the at least one processor is configured to select theML or AI application qualified for deployment on the edge compute unitfrom a repository including a plurality of ML or AI applications.

Aspect 25. The apparatus of Aspect 24, wherein the selected ML or AIapplication utilizes one or more pre-trained machine learning models,including the pre-trained machine learning model corresponding to therequest.

Aspect 26. The apparatus of any of Aspects 24 to 25, wherein the atleast one processor is configured to determine the ML or AI applicationis qualified for deployment on the edge compute unit based on one ormore of: a comparison between a computational hardware configurationrequirement of the ML or AI application and a respective computationalhardware deployment of the edge compute unit; or a comparison between aconnected edge asset requirement of the ML or AI application and arespective connected edge asset deployment associated with the edgecompute unit.

Aspect 27. The apparatus of Aspect 26, wherein the connected edge assetrequirement of the ML or AI application is indicative of one or moretypes of input data required for the ML or AI application, and whereinthe one or more types of input data required correspond to a set ofconnected edge asset types.

Aspect 28. The apparatus of any of Aspects 26 to 27, wherein: theconnected edge asset requirement is indicative of one or more differentmodalities of the one or more sensor data streams for inference usingthe pre-trained machine learning model; and the at least one processoris configured to perform the comparison between the connected edge assetrequirement and the respective connected edge asset deploymentassociated with the edge compute unit based on a determination of one ormore sensor data stream modalities available at the edge compute unit.

Aspect 29. The apparatus of any of Aspects 26 to 28, wherein theconnected edge asset requirement is indicative of one or more sensortypes for generating the one or more sensor data streams obtained at theedge location.

Aspect 30. The apparatus of any of Aspects 26 to 29, wherein theconnected edge asset requirement is indicative of one or more roboticassets associated with obtaining the one or more sensor data streams atthe edge location.

Aspect 31. The apparatus of any of Aspects 24 to 30, wherein: the ML orAI application is configured to generate as output one or more controlcommands for a respective edge device type; and the ML or AI applicationis qualified for deployment on the edge compute unit based on adetermination that the edge compute unit is communicatively coupled toat least one edge device of the respective edge device type and providedat the edge location.

Aspect 32. The apparatus of Aspect 31, wherein: the ML or AI applicationis configured to generate as output routing instructions for one or moredrones or robotic units provided at the edge location andcommunicatively coupled to the edge compute unit.

Aspect 33. The apparatus of any of Aspects 23 to 32, wherein: the edgecompute unit comprises an edge infrastructure node having self-containedstorage hardware, computational hardware, and connectivity hardwarewithin a single housing.

Aspect 34. The apparatus of Aspect 33, wherein the edge infrastructurenode is a containerized edge data center unit.

Aspect 35. The apparatus of any of Aspects 23 to 34, wherein the atleast one processor is further configured to: receive the request fromthe edge compute unit, wherein the request is received from the edgecompute unit using a satellite internet constellation connectivity linkassociated with one or more satellite transceivers of the edge computeunit.

Aspect 36. The apparatus of any of Aspects 23 to 35, wherein the atleast one processor is configured to transmit the pre-trained machinelearning model to the edge compute unit using satellite internetconstellation connectivity.

Aspect 37. The apparatus of any of Aspects 23 to 36, wherein the atleast one processor is configured to transmit the pre-trained machinelearning model to the edge compute unit from a cloud deployment of aplurality of ML or AI training clusters.

Aspect 38. The apparatus of Aspect 37, wherein: to obtain thepre-trained machine learning model, the at least one processor isconfigured to train a baseline machine learning model using the clouddeployment of the plurality of ML or AI training clusters; and to trainthe baseline machine learning model, the at least one processor isconfigured to perform training based at least in part on training datacorresponding to one or more sensor data streams at the edge locationand associated with the edge compute unit.

Aspect 39. The apparatus of any of Aspects 23 to 38, wherein the one ormore batch uploads of information are indicative of performance metricsassociated with the inference performed by the edge compute unit, andwherein the at least one processor is further configured to: analyze theperformance metrics associated with the inference performed by the edgecompute unit; and based on a determination that inference performance ofthe edge compute unit is below a configured threshold, generate andtransmit the updated machine learning model to the edge compute unit.

Aspect 40. The apparatus of Aspect 39, wherein the at least oneprocessor is further configured to: analyze the performance metricsassociated with the inference performed by the edge compute unit; andbased on a determination that inference performance of the edge computeunit is below a configured threshold, transmit to the edge compute unita command to perform one or more of model retraining or model finetuningfor the pre-trained machine learning model.

Aspect 41. The apparatus of Aspect 40, wherein the at least oneprocessor is configured to transmit, to the edge compute unit,supplemental information for performing the one or more of modelretraining or model finetuning.

Aspect 42. The apparatus of Aspect 41, wherein: the at least oneprocessor is configured to generate the supplemental information formodel retaining or model finetuning based on respective batch uploadinformation received from one or more additional edge compute unitsdifferent from the edge compute unit; and the one or more additionaledge compute units are configured to perform inference using the samepre-trained machine learning model.

Aspect 43. A method comprising operations according to any of Aspects 23to 42.

Aspect 44. A non-transitory computer-readable storage medium havingstored thereon instructions which, when executed by one or moreprocessors, cause the one or more processors to perform any of theoperations of Aspects 23 to 42.

Aspect 45. A method comprising: transmitting, from an edge compute unit,a request corresponding to a pre-trained machine learning model;receiving, from a cloud management platform and by the edge computeunit, the pre-trained machine learning model, wherein the edge computeunit is deployed to an edge location and configured to obtain one ormore sensor data streams at the edge location; transmitting, from theedge compute unit, one or more batch uploads of information associatedwith inference performed by the edge compute unit using the pre-trainedmachine learning model and the one or more sensor data streams, whereinthe one or more batch uploads of information are transmitted to thecloud management platform; and receiving, by the edge compute unit, oneor more updated machine learning models generated by the cloudmanagement platform responsive to the one or more batch uploads ofinformation, wherein the one or more updated machine learning models arebased on retraining or finetuning of the pre-trained machine learningmodel with the one or more batch uploads of information.

Aspect 46. The method of Aspect 45, wherein: the request is indicativeof a selection of a machine learning (ML) or artificial intelligence(AI) application qualified for deployment on the edge compute unit; andthe ML or AI application qualified for deployment on the edge computeunit is selected from a repository including a plurality of ML or AIapplications.

Aspect 47. The method of Aspect 46, wherein the selected ML or AIapplication utilizes one or more pre-trained machine learning models,including the pre-trained machine learning model corresponding to therequest.

Aspect 48. The method of any of Aspects 46 to 47, wherein the ML or AIapplication is qualified for deployment on the edge compute unit basedon one or more of: a comparison between a computational hardwareconfiguration requirement of the ML or AI application and a respectivecomputational hardware deployment of the edge compute unit; or acomparison between a connected edge asset requirement of the ML or AIapplication and a respective connected edge asset deployment associatedwith the edge compute unit.

Aspect 49. The method of Aspect 48, wherein the connected edge assetrequirement of the ML or AI application is indicative of one or moretypes of input data required for the ML or AI application, and whereinthe one or more types of input data required correspond to a set ofconnected edge asset types.

Aspect 50. The method of any of Aspects 48 to 49, wherein: the connectededge asset requirement is indicative of one or more different modalitiesof the one or more sensor data streams for inference using thepre-trained machine learning model; and the comparison between theconnected edge asset requirement and the respective connected edge assetdeployment associated with the edge compute unit is based on adetermination of one or more sensor data stream modalities available atthe edge compute unit.

Aspect 51. The method of any of Aspects 48 to 50, wherein the connectededge asset requirement is indicative of one or more sensor types forgenerating the one or more sensor data streams obtained at the edgelocation.

Aspect 52. The method of any of Aspects 48 to 51, wherein the connectededge asset requirement is indicative of one or more robotic assetsassociated with obtaining the one or more sensor data streams at theedge location.

Aspect 53. The method of any of Aspects 46 to 52, wherein: the ML or AIapplication is configured to generate as output one or more controlcommands for a respective edge device type; and the ML or AI applicationis qualified for deployment on the edge compute unit based on adetermination that the edge compute unit is communicatively coupled toat least one edge device of the respective edge device type and providedat the edge location.

Aspect 54. The method of Aspect 53, wherein: the ML or AI application isconfigured to generate as output routing instructions for one or moredrones or robotic units provided at the edge location andcommunicatively coupled to the edge compute unit.

Aspect 55. The method of any of Aspects 45 to 54, wherein: the edgecompute unit comprises an edge infrastructure node having self-containedstorage hardware, computational hardware, and connectivity hardwarewithin a single housing.

Aspect 56. The method of Aspect 55, wherein the edge infrastructure nodeis a containerized edge data center unit.

Aspect 57. The method of any of Aspects 45 to 56, further comprising:transmitting the request from the edge compute unit and to the cloudmanagement platform using a satellite internet constellationconnectivity link associated with one or more satellite transceivers ofthe edge compute unit.

Aspect 58. The method of any of Aspects 45 to 57, wherein thepre-trained machine learning model is received from the cloud managementplatform and by the edge compute unit using satellite internetconstellation connectivity.

Aspect 59. The method of any of Aspects 45 to 58, wherein thepre-trained machine learning model is received by the edge compute unitfrom a cloud deployment of a plurality of ML or AI training clusters,the cloud deployment associated with or included in the cloud managementplatform.

Aspect 60. The method of Aspect 59, wherein: the pre-trained machinelearning model comprises a baseline machine learning model trained bythe cloud deployment of the plurality of ML or AI training clusters; andthe baseline machine learning model is trained based at least in part ontraining data corresponding to the one or more sensor data streams atthe edge location, wherein the edge compute unit is configured totransmit the one or more sensor data streams to the cloud managementplatform.

Aspect 61. The method of any of Aspects 45 to 60, wherein the one ormore batch uploads of information are indicative of performance metricsassociated with the inference performed by the edge compute unit, andwherein the edge compute unit receives the one or more updated machinelearning models based on a determination that inference performance ofthe edge compute unit is below a configured threshold.

Aspect 62. The method of Aspect 61, further comprising: based on adetermination that inference performance of the edge compute unit isbelow a configured threshold, receiving from the cloud managementplatform and by the edge compute unit, a command to perform one or moreof model retraining or model finetuning for the pre-trained machinelearning model; and performing, by the edge compute unit, the commandedone or more of model retraining or model finetuning for the pre-trainedmachine learning model.

Aspect 63. The method of Aspect 62, further comprising receiving, fromthe cloud management platform and by the edge compute unit, supplementalinformation for performing the commanded one or more of model retrainingor model finetuning.

Aspect 64. The method of Aspect 63, wherein: the supplementalinformation for model retaining or model finetuning is generated basedon respective batch upload information received from one or moreadditional edge compute units different from the edge compute unit; andthe one or more additional edge compute units are configured to performinference using the same pre-trained machine learning model.

Aspect 65. An apparatus comprising means for performing any of theoperations of Aspects 45 to 64.

Aspect 66. A non-transitory computer-readable storage medium havingstored thereon instructions which, when executed by one or moreprocessors, cause the one or more processors to perform any of theoperations of Aspects 45 to 64.

Aspect 67. An apparatus of an edge compute unit, comprising: at leastone memory; and at least one processor coupled to the at least onememory, the at least one processor configured to: transmit, from theedge compute unit, a request corresponding to a pre-trained machinelearning model; receive, from a cloud management platform and by theedge compute unit, the pre-trained machine learning model, wherein theedge compute unit is deployed to an edge location and configured toobtain one or more sensor data streams at the edge location; transmit,from the edge compute unit, one or more batch uploads of informationassociated with inference performed by the edge compute unit using thepre-trained machine learning model and the one or more sensor datastreams, wherein the at least one processor is configured to transmitthe one or more batch uploads of information to the cloud managementplatform; and receive, by the edge compute unit, one or more updatedmachine learning models generated by the cloud management platformresponsive to the one or more batch uploads of information, wherein theone or more updated machine learning models are based on retraining orfinetuning of the pre-trained machine learning model with the one ormore batch uploads of information.

Aspect 68. The apparatus of Aspect 67, wherein: the request isindicative of a selection of a machine learning (ML) or artificialintelligence (AI) application qualified for deployment on the edgecompute unit; and the ML or AI application qualified for deployment onthe edge compute unit is selected from a repository including aplurality of ML or AI applications.

Aspect 69. The apparatus of Aspect 68, wherein the selected ML or AIapplication utilizes one or more pre-trained machine learning models,including the pre-trained machine learning model corresponding to therequest.

Aspect 70. The apparatus of any of Aspects 68 to 69, wherein the ML orAI application is qualified for deployment on the edge compute unitbased on one or more of: a comparison between a computational hardwareconfiguration requirement of the ML or AI application and a respectivecomputational hardware deployment of the edge compute unit; or acomparison between a connected edge asset requirement of the ML or AIapplication and a respective connected edge asset deployment associatedwith the edge compute unit.

Aspect 71. The apparatus of Aspect 70, wherein the connected edge assetrequirement of the ML or AI application is indicative of one or moretypes of input data required for the ML or AI application, and whereinthe one or more types of input data required correspond to a set ofconnected edge asset types.

Aspect 72. The apparatus of any of Aspects 70 to 71, wherein: theconnected edge asset requirement is indicative of one or more differentmodalities of the one or more sensor data streams for inference usingthe pre-trained machine learning model; and the comparison between theconnected edge asset requirement and the respective connected edge assetdeployment associated with the edge compute unit is based on adetermination of one or more sensor data stream modalities available atthe edge compute unit.

Aspect 73. The apparatus of any of Aspects 70 to 72, wherein theconnected edge asset requirement is indicative of one or more sensortypes for generating the one or more sensor data streams obtained at theedge location.

Aspect 74. The apparatus of any of Aspects 70 to 73, wherein theconnected edge asset requirement is indicative of one or more roboticassets associated with obtaining the one or more sensor data streams atthe edge location.

Aspect 75. The apparatus of any of Aspects 68 to 74, wherein: the ML orAI application is configured to generate as output one or more controlcommands for a respective edge device type; and the ML or AI applicationis qualified for deployment on the edge compute unit based on adetermination that the edge compute unit is communicatively coupled toat least one edge device of the respective edge device type and providedat the edge location.

Aspect 76. The apparatus of Aspect 75, wherein: the ML or AI applicationis configured to generate as output routing instructions for one or moredrones or robotic units provided at the edge location andcommunicatively coupled to the edge compute unit.

Aspect 77. The apparatus of any of Aspects 67 to 76, wherein: the edgecompute unit comprises an edge infrastructure node having self-containedstorage hardware, computational hardware, and connectivity hardwarewithin a single housing.

Aspect 78. The apparatus of Aspect 77, wherein the edge infrastructurenode is a containerized edge data center unit.

Aspect 79. The apparatus of any of Aspects 67 to 78, wherein, totransmit the request from the edge compute unit and to the cloudmanagement platform, the at least one processor is configured to: use asatellite internet constellation connectivity link associated with oneor more satellite transceivers of the edge compute unit to transmit therequest.

Aspect 80. The apparatus of Aspect 67, wherein, to receive thepre-trained machine learning model, the at least one processor isconfigured to: use satellite internet constellation connectivity toreceive the pre-trained machine learning model from the cloud managementplatform.

Aspect 81. The apparatus of any of Aspects 67 to 80, wherein, to receivethe pre-trained machine learning model, the at least one processor isconfigured to: receive the pre-trained machine learning model from acloud deployment of a plurality of ML or AI training clusters, the clouddeployment associated with or included in the cloud management platform.

Aspect 82. The apparatus of Aspect 81, wherein: the pre-trained machinelearning model comprises a baseline machine learning model trained bythe cloud deployment of the plurality of ML or AI training clusters; andthe baseline machine learning model is trained based at least in part ontraining data corresponding to the one or more sensor data streams atthe edge location, wherein the at least one processor is configured totransmit the one or more sensor data streams to the cloud managementplatform.

Aspect 83. The apparatus of any of Aspects 67 to 82, wherein the one ormore batch uploads of information are indicative of performance metricsassociated with the inference performed by the edge compute unit, andwherein the at least one processor is configured to receive the one ormore updated machine learning models based on a determination thatinference performance of the edge compute unit is below a configuredthreshold.

Aspect 84. The apparatus of Aspect 83, wherein the at least oneprocessor is further configured to: receive, from the cloud managementplatform and based on a determination that inference performance of theedge compute unit is below a configured threshold, a command to performone or more of model retraining or model finetuning for the pre-trainedmachine learning model; and perform the commanded one or more of modelretraining or model finetuning for the pre-trained machine learningmodel.

Aspect 85. The apparatus of Aspect 84, wherein the at least oneprocessor is further configured to: receive, from the cloud managementplatform, supplemental information for performing the commanded one ormore of model retraining or model finetuning.

Aspect 86. The apparatus of Aspect 85, wherein: the supplementalinformation for model retaining or model finetuning is generated basedon respective batch upload information received from one or moreadditional edge compute units different from the edge compute unit; andthe one or more additional edge compute units are configured to performinference using the same pre-trained machine learning model.

Aspect 87. A method comprising operations according to any of Aspects 67to 42.

Aspect 88. A non-transitory computer-readable storage medium havingstored thereon instructions which, when executed by one or moreprocessors, cause the one or more processors to perform any of theoperations of Aspects 67 to 86.

Aspect 89. A method comprising: receiving monitoring information fromeach respective edge compute unit of a plurality of edge compute units,wherein the monitoring information includes information associated withone or more machine learning (ML) or artificial intelligence (AI)workloads implemented by the respective edge compute unit; receivingrespective status information corresponding to a plurality of connectededge assets, wherein each connected edge asset is associated with one ormore edge compute units of the plurality of edge compute units, andwherein the plurality of edge compute units and the plurality ofconnected edge assets are included in a fleet of edge devices;displaying, using a remote fleet management graphical user interface(GUI), at least a portion of the monitoring information or the statusinformation corresponding to a selected subset of the fleet of edgedevices, wherein the selected subset is determined based on one or moreuser selection inputs to the remote fleet management GUI; receiving,using the remote fleet management GUI, one or more user configurationinputs indicative of an updated configuration for at least one workloadassociated with at least one workload of at least one edge compute unitof the selected subset of the fleet of edge devices, the at least oneworkload corresponding to a pre-trained ML or AI model deployed on theat least one edge compute unit; and transmitting, from a cloud computingenvironment associated with the remote fleet management GUI, controlinformation corresponding to the updated configuration, wherein thecontrol information is transmitted to the at least one edge compute unitof the selected sub set.

Aspect 90. The method of Aspect 89, wherein the one or more userconfiguration inputs are indicative of an updated configuration for arespective ML or AI workload of the one or more ML or AI workloads.

Aspect 91. The method of any of Aspects 89 to 90, wherein the updatedconfiguration for the respective ML or AI workload corresponds to apre-trained ML or AI model associated with the respective ML or AIworkload.

Aspect 92. The method of Aspect 91, wherein the updated configuration isconfigured to cause the at least one edge compute unit of the selectedsubset to perform local retraining of the pre-trained ML or AI model atan edge location of the at least one edge compute unit.

Aspect 93. The method of Aspect 92, wherein the updated configurationfurther includes retraining information for the local retraining of thepre-trained ML or AI model at the edge location.

Aspect 94. The method of Aspect 93, wherein the retraining informationis generated by the cloud computing environment based on informationobtained across the plurality of edge compute units included in thefleet of edge devices.

Aspect 95. The method of any of Aspects 91 to 94, wherein the updatedconfiguration is configured to cause the at least one edge compute unitof the selected subset to perform local finetuning of the pre-trained MLor AI model at an edge location of the at least one edge compute unit.

Aspect 96. The method of Aspect 95, wherein the updated configurationfurther includes finetuning information for the local finetuning of thepre-trained ML or AI model at the edge location.

Aspect 97. The method of Aspect 96, wherein the finetuning informationis generated by the cloud computing environment based on informationobtained across the plurality of edge compute units included in thefleet of edge devices.

Aspect 98. The method of any of Aspects 91 to 97, wherein the updatedconfiguration is configured to cause a subset of edge compute units ofthe plurality of edge compute units of the fleet of edge devices toperform distributed retraining of the pre-trained ML or AI model, andwherein the updated configuration information includes orchestrationinformation for distributing a retraining workload across the respectiveedge compute units of the subset of edge compute units.

Aspect 99. The method of any of Aspects 91 to 98, wherein the updatedconfiguration is configured to cause a subset of edge compute units ofthe plurality of edge compute units of the fleet of edge devices toperform distributed finetuning of the pre-trained ML or AI model, andwherein the updated configuration information includes orchestrationinformation for distributing a finetuning workload across the respectiveedge compute units of the subset of edge compute units.

Aspect 100. The method of any of Aspects 89 to 99, wherein the one ormore user configuration inputs are indicative of an updated networkconnectivity configuration applicable to at least a portion of theselected subset of the fleet of edge devices.

Aspect 101. The method of Aspect 100, wherein: the updated networkconnectivity configuration corresponds to a local edge networkimplemented at an edge deployment location of a plurality of edgedeployment locations for the fleet of edge devices; and the local edgenetwork is implemented at the edge deployment location by acorresponding edge compute unit of the fleet of edge devices.

Aspect 102. The method of Aspect 101, wherein the local edge network isconfigured for wireless communications between the corresponding edgecompute unit and the respective connected edge assets associated withthe corresponding edge compute unit.

Aspect 103. The method of Aspect 102, wherein the updated networkconnectivity configuration corresponds to updated provisioninginformation for deploying one or more additional connected edge assetsto the fleet of edge devices and within the edge deployment location.

Aspect 104. The method of any of Aspects 100 to 103, wherein the one ormore user configuration inputs are indicative of an updated networkconnectivity configuration applicable to one or more internet backhaullinks between the fleet of edge devices and the cloud computingenvironment associated with the remote fleet management GUI.

Aspect 105. The method of Aspect 104, wherein each internet backhaullink of the one or more internet backhaul links is configured between arespective edge deployment location of a plurality of edge deploymentlocations for the fleet of edge devices and the cloud computingenvironment associated with the remote fleet management GUI.

Aspect 106. The method of any of Aspects 104 to 105, wherein eachinternet backhaul link of the one or more internet backhaul linkscomprises a satellite internet constellation backhaul link between atleast one edge device of the fleet of edge devices and at least onesatellite of a satellite internet constellation.

Aspect 107. The method of Aspect 106, wherein the one or more userconfiguration inputs are indicative of updated subscription informationbetween the satellite internet constellation and a satellite internetconstellation transceiver terminal associated with an edge compute unitof the fleet of edge devices.

Aspect 108. The method of any of Aspects 100 to 107, wherein the updatednetwork connectivity configuration corresponds to a Software-DefinedNetworking (SDN) layer associated with the fleet of edge devices, andwherein the updated network connectivity configuration is indicative ofone or more updated SDN layer configurations for at least a portion ofthe plurality of edge compute units of the fleet of edge devices.

Aspect 109. An apparatus comprising means for performing any of theoperations of Aspects 89 to 108.

Aspect 110. A non-transitory computer-readable storage medium havingstored thereon instructions which, when executed by one or moreprocessors, cause the one or more processors to perform any of theoperations of Aspects 89 to 108.

Aspect 111. An apparatus comprising: at least one memory; and at leastone processor coupled to the at least one memory, the at least oneprocessor configured to: receive monitoring information from eachrespective edge compute unit of a plurality of edge compute units,wherein the monitoring information includes information associated withone or more machine learning (ML) or artificial intelligence (AI)workloads implemented by the respective edge compute unit; receiverespective status information corresponding to a plurality of connectededge assets, wherein each connected edge asset is associated with one ormore edge compute units of the plurality of edge compute units, andwherein the plurality of edge compute units and the plurality ofconnected edge assets are included in a fleet of edge devices; display,using a remote fleet management graphical user interface (GUI), at leasta portion of the monitoring information or the status informationcorresponding to a selected subset of the fleet of edge devices, whereinthe selected subset is determined based on one or more user selectioninputs to the remote fleet management GUI; receive, using the remotefleet management GUI, one or more user configuration inputs indicativeof an updated configuration for at least one workload associated with atleast one workload of at least one edge compute unit of the selectedsubset of the fleet of edge devices, the at least one workloadcorresponding to a pre-trained ML or AI model deployed on the at leastone edge compute unit; and transmit, from a cloud computing environmentassociated with the remote fleet management GUI, control informationcorresponding to the updated configuration, wherein the controlinformation is transmitted to the at least one edge compute unit of theselected subset.

Aspect 112. The apparatus of Aspect 111, wherein the one or more userconfiguration inputs are indicative of an updated configuration for arespective ML or AI workload of the one or more ML or AI workloads.

Aspect 113. The apparatus of any of Aspects 111 to 112, wherein theupdated configuration for the respective ML or AI workload correspondsto a pre-trained ML or AI model associated with the respective ML or AIworkload.

Aspect 114. The apparatus of Aspect 113, wherein the updatedconfiguration is configured to cause the at least one edge compute unitof the selected subset to perform local retraining of the pre-trained MLor AI model at an edge location of the at least one edge compute unit.

Aspect 115. The apparatus of Aspect 114, wherein the updatedconfiguration further includes retraining information for the localretraining of the pre-trained ML or AI model at the edge location.

Aspect 116. The apparatus of Aspect 115, wherein the retraininginformation is generated by the cloud computing environment based oninformation obtained across the plurality of edge compute units includedin the fleet of edge devices.

Aspect 117. The apparatus of any of Aspects 113 to 116, wherein theupdated configuration is configured to cause the at least one edgecompute unit of the selected subset to perform local finetuning of thepre-trained ML or AI model at an edge location of the at least one edgecompute unit.

Aspect 118. The apparatus of Aspect 117, wherein the updatedconfiguration further includes finetuning information for the localfinetuning of the pre-trained ML or AI model at the edge location.

Aspect 119. The apparatus of Aspect 118, wherein the finetuninginformation is generated by the cloud computing environment based oninformation obtained across the plurality of edge compute units includedin the fleet of edge devices.

Aspect 120. The apparatus of any of Aspects 113 to 119, wherein theupdated configuration is configured to cause a subset of edge computeunits of the plurality of edge compute units of the fleet of edgedevices to perform distributed retraining of the pre-trained ML or AImodel, and wherein the updated configuration information includesorchestration information for distributing a retraining workload acrossthe respective edge compute units of the subset of edge compute units.

Aspect 121. The apparatus of any of Aspects 113 to 120, wherein theupdated configuration is configured to cause a subset of edge computeunits of the plurality of edge compute units of the fleet of edgedevices to perform distributed finetuning of the pre-trained ML or AImodel, and wherein the updated configuration information includesorchestration information for distributing a finetuning workload acrossthe respective edge compute units of the subset of edge compute units.

Aspect 122. The apparatus of any of Aspects 111 to 121, wherein the oneor more user configuration inputs are indicative of an updated networkconnectivity configuration applicable to at least a portion of theselected subset of the fleet of edge devices.

Aspect 123. The apparatus of Aspect 122, wherein: the updated networkconnectivity configuration corresponds to a local edge networkimplemented at an edge deployment location of a plurality of edgedeployment locations for the fleet of edge devices; and the local edgenetwork is implemented at the edge deployment location by acorresponding edge compute unit of the fleet of edge devices.

Aspect 124. The apparatus of Aspect 123, wherein the local edge networkis configured for wireless communications between the corresponding edgecompute unit and the respective connected edge assets associated withthe corresponding edge compute unit.

Aspect 125. The apparatus of Aspect 124, wherein the updated networkconnectivity configuration corresponds to updated provisioninginformation for deploying one or more additional connected edge assetsto the fleet of edge devices and within the edge deployment location.

Aspect 126. The apparatus of any of Aspects 122 to 125, wherein the oneor more user configuration inputs are indicative of an updated networkconnectivity configuration applicable to one or more internet backhaullinks between the fleet of edge devices and the cloud computingenvironment associated with the remote fleet management GUI.

Aspect 127. The apparatus of Aspect 126, wherein each internet backhaullink of the one or more internet backhaul links is configured between arespective edge deployment location of a plurality of edge deploymentlocations for the fleet of edge devices and the cloud computingenvironment associated with the remote fleet management GUI.

Aspect 128. The apparatus of any of Aspects 126 to 127, wherein eachinternet backhaul link of the one or more internet backhaul linkscomprises a satellite internet constellation backhaul link between atleast one edge device of the fleet of edge devices and at least onesatellite of a satellite internet constellation.

Aspect 129. The apparatus of Aspect 129, wherein the one or more userconfiguration inputs are indicative of updated subscription informationbetween the satellite internet constellation and a satellite internetconstellation transceiver terminal associated with an edge compute unitof the fleet of edge devices.

Aspect 130. The apparatus of any of Aspects 122 to 129, wherein theupdated network connectivity configuration corresponds to aSoftware-Defined Networking (SDN) layer associated with the fleet ofedge devices, and wherein the updated network connectivity configurationis indicative of one or more updated SDN layer configurations for atleast a portion of the plurality of edge compute units of the fleet ofedge devices.

Aspect 131. A method comprising operations according to any of Aspects111 to 130.

Aspect 132. A non-transitory computer-readable storage medium havingstored thereon instructions which, when executed by one or moreprocessors, cause the one or more processors to perform any of theoperations of Aspects 111 to 130.

Aspect 133. A method comprising: receiving monitoring information fromeach respective edge compute unit of a plurality of edge compute units,wherein the monitoring information includes information associated withone or more machine learning (ML) or artificial intelligence (AI)workloads implemented by the respective edge compute unit; receivingrespective status information corresponding to a plurality of connectededge assets, wherein each connected edge asset is associated with one ormore edge compute units of the plurality of edge compute units, andwherein the plurality of edge compute units and the plurality ofconnected edge assets are included in a fleet of edge devices;displaying, using a remote fleet management graphical user interface(GUI), at least a portion of the monitoring information or the statusinformation corresponding to a selected subset of the fleet of edgedevices, wherein the selected subset is determined based on one or moreuser selection inputs to the remote fleet management GUI; receiving,using the remote fleet management GUI, one or more user configurationinputs indicative of an updated configuration associated with at leastone edge compute unit of the selected subset of the fleet of edgedevices; and transmitting, from a cloud computing environment associatedwith the remote fleet management GUI, control information correspondingto the updated configuration, wherein the control information istransmitted to the at least one edge compute unit of the selectedsubset.

Aspect 134. The method of Aspect 133, wherein: the one or more userselection inputs are indicative of one or more edge deployment locationsassociated with the fleet of edge devices.

Aspect 135. The method of Aspect 134, wherein each respective edgedeployment location of the one or more edge deployment locationsincludes at least one of: an edge compute unit of the plurality of edgecompute units; or a satellite internet constellation transceiverconfigured to provide internet backhaul communications between therespective edge deployment location and the cloud computing environmentassociated with the remote fleet management GUI.

Aspect 136. The method of Aspect 135, wherein: the selected subset ofthe plurality of edge compute units comprises respective edge computeunits deployed at one of the one or more edge deployment locations; andthe selected subset of the plurality of connected edge assets comprisesrespective connected edge assets deployed at one of the one or more edgedeployment locations.

Aspect 137. The method of any of Aspects 133 to 136, wherein displayingthe at least a portion of the monitoring information or the statusinformation corresponding to the selected subset of the fleet of edgedevices comprises: receiving, using the remote fleet management GUI, theone or more user selection inputs; determining a first filtered subsetof the plurality of edge compute units and a second filtered subset ofthe plurality of connected edge assets, the first filtered subset andthe second filtered subset based on filtering selection informationincluded in the one or more user selection inputs; and outputting fordisplay, the at least a portion of the monitoring information or thestatus information corresponding to the first filtered subset of edgecompute units and the second filtered subset of connected edge assets.

Aspect 138. The method of any of Aspects 133 to 137, further comprising:receiving, using the remote fleet management GUI, the one or more userselection inputs, each respective user selection input of the one ormore user selection inputs indicative of a filtering selectioncorresponding to a respective dimension of the monitoring informationand the status information for the fleet of edge devices; and updating adisplay output of the remote fleet management GUI based on the filteringselection indicated by each respective user selection input.

Aspect 139. The method of any of Aspects 133 to 138, wherein the remotefleet management GUI comprises a single pane of glass interfacecorresponding to the respective monitoring information and therespective status information for the edge compute units and theconnected edge assets of the fleet of edge devices.

Aspect 140. The method of Aspect 139, wherein the single pane of glassinterface includes one or more user interface input elements forreceiving the one or more user configuration inputs.

Aspect 141. The method of any of Aspects 133 to 140, wherein one or moreof the remote fleet management GUI or the cloud computing environmentassociated with the remote fleet management GUI corresponds to aplurality of user accounts uniquely associated with and provisioned foraccess to the monitoring information or the status informationcorresponding to the fleet of edge devices.

Aspect 142. The method of any of Aspects 133 to 141, wherein the fleetof edge devices is associated with a plurality of different edgedeployment locations, each edge deployment location associated with arespective first subset of the plurality of edge compute units and arespective second subset of the plurality of connected edge assets.

Aspect 143. The method of any of Aspects 133 to 142, wherein themonitoring information includes a plurality of environmental sensor datastreams corresponding to an internal environment of the respective edgecompute unit.

Aspect 144. The method of any of Aspects 133 to 143, wherein therespective edge compute unit comprises a containerized edge data centerunit including self-contained storage hardware, computational hardware,and connectivity hardware.

Aspect 145. The method of Aspect 144, wherein: the plurality ofenvironmental sensor data streams correspond to an internal environmentof the containerized edge data center unit; and the monitoringinformation further includes utilization information or health statusinformation for one or more of the self-contained storage hardware,computational hardware, or connectivity hardware of the containerizededge data center unit.

Aspect 146. The method of any of Aspects 133 to 145, wherein receivingthe respective status information corresponding to the plurality ofconnected edge assets comprises: receiving, from a first edge computeunit of the plurality of edge compute units, respective statusinformation corresponding to a first subset of the plurality ofconnected edge assets, wherein the first subset of connected edge assetsis associated with the first edge compute unit and a same first edgelocation as the first edge compute unit; and receiving, from a secondedge compute unit of the plurality of edge compute units, respectivestatus information corresponding to a second subset of the plurality ofconnected edge assets, wherein the second subset of connected edgeassets is associated with the second edge compute unit and a same secondedge location as the second edge compute unit.

Aspect 147. The method of any of Aspects 133 to 146, wherein therespective status information corresponding to the plurality ofconnected edge assets is indicative of one or more of health statusinformation for a connected edge asset or connectivity statusinformation for a connected edge asset.

Aspect 148. The method of Aspect 147, wherein the connectivity statusinformation is indicative of a local edge network connectivity statusbetween the connected edge asset and a corresponding edge compute unitof the plurality of edge compute units.

Aspect 149. The method of Aspect 148, wherein the corresponding edgecompute unit is associated with the connected edge asset and provides alocal edge network for connectivity with the connected edge asset.

Aspect 150. The method of any of Aspects 133 to 149, wherein theplurality of connected edge assets include one or more of: one or moresatellite internet constellation transceiver units; one or more cameras;one or more local edge sensors; one or more deployable robotic unitscontrollable by a respective edge compute unit of the plurality of edgecompute units; one or more drone units controllable by a respective edgecompute unit of the plurality of edge compute units; or one or morevehicles associated with a respective edge compute unit of the pluralityof edge compute units; and wherein each connected edge asset of theplurality of edge assets is communicatively coupled with an edge computeunit of the plurality of edge compute units.

Aspect 151. The method of any of Aspects 133 to 150, wherein: the one ormore user configuration inputs are indicative of an updated networkconnectivity configuration applicable to at least a portion of theselected subset of the fleet of edge devices; the updated networkconnectivity configuration corresponds to a configuration of redundantinternet backhaul links between the fleet of edge devices and the cloudcomputing environment associated with the remote fleet management GUI;and the redundant internet backhaul links include satellite internetconstellation internet backhaul links, 4G or 5G cellular internetbackhaul links, and fiber optic internet backhaul links.

Aspect 152. The method of Aspect 151, wherein the updated networkconnectivity configuration is indicative of one or more updated networkvirtualization parameters for network virtualization across at least thefiber optic internet backhaul links and the satellite internetconstellation internet backhaul links.

Aspect 153. An apparatus comprising means for performing any of theoperations of Aspects 133 to 152.

Aspect 154. A non-transitory computer-readable storage medium havingstored thereon instructions which, when executed by one or moreprocessors, cause the one or more processors to perform any of theoperations of Aspects 133 to 152.

Aspect 155. An apparatus comprising: at least one memory; and at leastone processor coupled to the at least one memory, the at least oneprocessor configured to: receive monitoring information from eachrespective edge compute unit of a plurality of edge compute units,wherein the monitoring information includes information associated withone or more machine learning (ML) or artificial intelligence (AI)workloads implemented by the respective edge compute unit; receiverespective status information corresponding to a plurality of connectededge assets, wherein each connected edge asset is associated with one ormore edge compute units of the plurality of edge compute units, andwherein the plurality of edge compute units and the plurality ofconnected edge assets are included in a fleet of edge devices; display,using a remote fleet management graphical user interface (GUI), at leasta portion of the monitoring information or the status informationcorresponding to a selected subset of the fleet of edge devices, whereinthe selected subset is determined based on one or more user selectioninputs to the remote fleet management GUI; receive, using the remotefleet management GUI, one or more user configuration inputs indicativeof an updated configuration associated with at least one edge computeunit of the selected subset of the fleet of edge devices; and transmit,from a cloud computing environment associated with the remote fleetmanagement GUI, control information corresponding to the updatedconfiguration, wherein the control information is transmitted to the atleast one edge compute unit of the selected subset.

Aspect 156. The apparatus of Aspect 155, wherein: the one or more userselection inputs are indicative of one or more edge deployment locationsassociated with the fleet of edge devices.

Aspect 157. The apparatus of Aspect 156, wherein each respective edgedeployment location of the one or more edge deployment locationsincludes at least one of: an edge compute unit of the plurality of edgecompute units; or a satellite internet constellation transceiverconfigured to provide internet backhaul communications between therespective edge deployment location and the cloud computing environmentassociated with the remote fleet management GUI.

Aspect 158. The apparatus of Aspect 157, wherein: the selected subset ofthe plurality of edge compute units comprises respective edge computeunits deployed at one of the one or more edge deployment locations; andthe selected subset of the plurality of connected edge assets comprisesrespective connected edge assets deployed at one of the one or more edgedeployment locations.

Aspect 159. The apparatus of any of Aspects 155 to 158, whereindisplaying the at least a portion of the monitoring information or thestatus information corresponding to the selected subset of the fleet ofedge devices comprises: receiving, using the remote fleet managementGUI, the one or more user selection inputs; determining a first filteredsubset of the plurality of edge compute units and a second filteredsubset of the plurality of connected edge assets, the first filteredsubset and the second filtered subset based on filtering selectioninformation included in the one or more user selection inputs; andoutputting for display, the at least a portion of the monitoringinformation or the status information corresponding to the firstfiltered subset of edge compute units and the second filtered subset ofconnected edge assets.

Aspect 160. The apparatus of any of Aspects 155 to 159, furthercomprising: receiving, using the remote fleet management GUI, the one ormore user selection inputs, each respective user selection input of theone or more user selection inputs indicative of a filtering selectioncorresponding to a respective dimension of the monitoring informationand the status information for the fleet of edge devices; and updating adisplay output of the remote fleet management GUI based on the filteringselection indicated by each respective user selection input.

Aspect 161. The apparatus of any of Aspects 155 to 160, wherein theremote fleet management GUI comprises a single pane of glass interfacecorresponding to the respective monitoring information and therespective status information for the edge compute units and theconnected edge assets of the fleet of edge devices.

Aspect 162. The apparatus of Aspect 161, wherein the single pane ofglass interface includes one or more user interface input elements forreceiving the one or more user configuration inputs.

Aspect 163. The apparatus of any of Aspects 155 to 162, wherein one ormore of the remote fleet management GUI or the cloud computingenvironment associated with the remote fleet management GUI correspondsto a plurality of user accounts uniquely associated with and provisionedfor access to the monitoring information or the status informationcorresponding to the fleet of edge devices.

Aspect 164. The apparatus of any of Aspects 155 to 163, wherein thefleet of edge devices is associated with a plurality of different edgedeployment locations, each edge deployment location associated with arespective first subset of the plurality of edge compute units and arespective second subset of the plurality of connected edge assets.

Aspect 165. The apparatus of any of Aspects 155 to 164, wherein themonitoring information includes a plurality of environmental sensor datastreams corresponding to an internal environment of the respective edgecompute unit.

Aspect 166. The apparatus of any of Aspects 155 to 165, wherein therespective edge compute unit comprises a containerized edge data centerunit including self-contained storage hardware, computational hardware,and connectivity hardware.

Aspect 167. The apparatus of Aspect 166, wherein: the plurality ofenvironmental sensor data streams correspond to an internal environmentof the containerized edge data center unit; and the monitoringinformation further includes utilization information or health statusinformation for one or more of the self-contained storage hardware,computational hardware, or connectivity hardware of the containerizededge data center unit.

Aspect 168. The apparatus of any of Aspects 155 to 167, whereinreceiving the respective status information corresponding to theplurality of connected edge assets comprises: receiving, from a firstedge compute unit of the plurality of edge compute units, respectivestatus information corresponding to a first subset of the plurality ofconnected edge assets, wherein the first subset of connected edge assetsis associated with the first edge compute unit and a same first edgelocation as the first edge compute unit; and receiving, from a secondedge compute unit of the plurality of edge compute units, respectivestatus information corresponding to a second subset of the plurality ofconnected edge assets, wherein the second subset of connected edgeassets is associated with the second edge compute unit and a same secondedge location as the second edge compute unit.

Aspect 169. The apparatus of any of Aspects 155 to 168, wherein therespective status information corresponding to the plurality ofconnected edge assets is indicative of one or more of health statusinformation for a connected edge asset or connectivity statusinformation for a connected edge asset.

Aspect 170. The apparatus of Aspect 169, wherein the connectivity statusinformation is indicative of a local edge network connectivity statusbetween the connected edge asset and a corresponding edge compute unitof the plurality of edge compute units.

Aspect 171. The apparatus of Aspect 170, wherein the corresponding edgecompute unit is associated with the connected edge asset and provides alocal edge network for connectivity with the connected edge asset.

Aspect 172. The apparatus of any of Aspects 155 to 171, wherein theplurality of connected edge assets include one or more of: one or moresatellite internet constellation transceiver units; one or more cameras;one or more local edge sensors; one or more deployable robotic unitscontrollable by a respective edge compute unit of the plurality of edgecompute units; one or more drone units controllable by a respective edgecompute unit of the plurality of edge compute units; or one or morevehicles associated with a respective edge compute unit of the pluralityof edge compute units; and wherein each connected edge asset of theplurality of edge assets is communicatively coupled with an edge computeunit of the plurality of edge compute units.

Aspect 173. The apparatus of any of Aspects 155 to 172, wherein: the oneor more user configuration inputs are indicative of an updated networkconnectivity configuration applicable to at least a portion of theselected subset of the fleet of edge devices; the updated networkconnectivity configuration corresponds to a configuration of redundantinternet backhaul links between the fleet of edge devices and the cloudcomputing environment associated with the remote fleet management GUI;and the redundant internet backhaul links include satellite internetconstellation internet backhaul links, 4G or 5G cellular internetbackhaul links, and fiber optic internet backhaul links.

Aspect 174. The apparatus of Aspect 173, wherein the updated networkconnectivity configuration is indicative of one or more updated networkvirtualization parameters for network virtualization across at least thefiber optic internet backhaul links and the satellite internetconstellation internet backhaul links.

Aspect 175. A method comprising operations according to any of Aspects155 to 174.

Aspect 176. A non-transitory computer-readable storage medium havingstored thereon instructions which, when executed by one or moreprocessors, cause the one or more processors to perform any of theoperations of Aspects 155 to 174.

What is claimed is:
 1. A method comprising: receiving monitoringinformation from each respective edge compute unit of a plurality ofedge compute units, wherein the monitoring information includesinformation associated with one or more machine learning (ML) orartificial intelligence (AI) workloads implemented by the respectiveedge compute unit; receiving respective status information correspondingto a plurality of connected edge assets, wherein each connected edgeasset is associated with one or more edge compute units of the pluralityof edge compute units, and wherein the plurality of edge compute unitsand the plurality of connected edge assets are included in a fleet ofedge devices; displaying, using a remote fleet management graphical userinterface (GUI), at least a portion of the monitoring information or thestatus information corresponding to a selected subset of the fleet ofedge devices, wherein the selected subset is determined based on one ormore user selection inputs to the remote fleet management GUI;receiving, using the remote fleet management GUI, one or more userconfiguration inputs indicative of an updated configuration for arespective ML or AI workload of at least one edge compute unit of theselected subset of the fleet of edge devices, the respective ML or AIworkload corresponding to a pre-trained ML or AI model deployed on theat least one edge compute unit, wherein the updated configuration:corresponds to the pre-trained ML or AI model and is configured to causea subset of edge compute units of the fleet of edge devices to performdistributed retraining of the pre-trained ML or AI model; and includesorchestration information for distributing a retraining workload acrossthe respective edge compute units of the subset of edge compute units;and transmitting, from a cloud computing environment associated with theremote fleet management GUI, control information corresponding to theupdated configuration, wherein the control information is transmitted tothe at least one edge compute unit of the selected subset.
 2. The methodof claim 1, wherein the one or more user configuration inputs areindicative of an updated configuration for a respective ML or AIworkload of the one or more ML or AI workloads.
 3. The method of claim1, wherein the updated configuration for the respective ML or AIworkload corresponds to a pre-trained ML or AI model associated with therespective ML or AI workload.
 4. The method of claim 3, wherein theupdated configuration is configured to cause the at least one edgecompute unit of the selected subset to perform local retraining of thepre-trained ML or AI model at an edge location of the at least one edgecompute unit.
 5. The method of claim 4, wherein the updatedconfiguration further includes retraining information for the localretraining of the pre-trained ML or AI model at the edge location. 6.The method of claim 5, wherein the retraining information is generatedby the cloud computing environment based on information obtained acrossthe plurality of edge compute units included in the fleet of edgedevices.
 7. The method of claim 3, wherein the updated configuration isconfigured to cause the at least one edge compute unit of the selectedsubset to perform local finetuning of the pre-trained ML or AI model atan edge location of the at least one edge compute unit.
 8. The method ofclaim 7, wherein the updated configuration further includes finetuninginformation for the local finetuning of the pre-trained ML or AI modelat the edge location.
 9. The method of claim 8, wherein the finetuninginformation is generated by the cloud computing environment based oninformation obtained across the plurality of edge compute units includedin the fleet of edge devices.
 10. The method of claim 3, wherein theupdated configuration is configured to cause a subset of edge computeunits of the plurality of edge compute units of the fleet of edgedevices to perform distributed finetuning of the pre-trained ML or AImodel, and wherein the updated configuration information includesorchestration information for distributing a finetuning workload acrossthe respective edge compute units of the subset of edge compute units.11. The method of claim 1, wherein the one or more user configurationinputs are indicative of an updated network connectivity configurationapplicable to at least a portion of the selected subset of the fleet ofedge devices.
 12. The method of claim 11, wherein: the updated networkconnectivity configuration corresponds to a local edge networkimplemented at an edge deployment location of a plurality of edgedeployment locations for the fleet of edge devices; and the local edgenetwork is implemented at the edge deployment location by acorresponding edge compute unit of the fleet of edge devices.
 13. Themethod of claim 12, wherein the local edge network is configured forwireless communications between the corresponding edge compute unit andthe respective connected edge assets associated with the correspondingedge compute unit.
 14. The method of claim 13, wherein the updatednetwork connectivity configuration corresponds to updated provisioninginformation for deploying one or more additional connected edge assetsto the fleet of edge devices and within the edge deployment location.15. The method of claim 11, wherein the one or more user configurationinputs are indicative of an updated network connectivity configurationapplicable to one or more internet backhaul links between the fleet ofedge devices and the cloud computing environment associated with theremote fleet management GUI.
 16. The method of claim 15, wherein eachinternet backhaul link of the one or more internet backhaul links isconfigured between a respective edge deployment location of a pluralityof edge deployment locations for the fleet of edge devices and the cloudcomputing environment associated with the remote fleet management GUI.17. The method of claim 15, wherein each internet backhaul link of theone or more internet backhaul links comprises a satellite internetconstellation backhaul link between at least one edge device of thefleet of edge devices and at least one satellite of a satellite internetconstellation.
 18. The method of claim 17, wherein the one or more userconfiguration inputs are indicative of updated subscription informationbetween the satellite internet constellation and a satellite internetconstellation transceiver terminal associated with an edge compute unitof the fleet of edge devices.
 19. The method of claim 11, wherein theupdated network connectivity configuration corresponds to aSoftware-Defined Networking (SDN) layer associated with the fleet ofedge devices, and wherein the updated network connectivity configurationis indicative of one or more updated SDN layer configurations for atleast a portion of the plurality of edge compute units of the fleet ofedge devices.